Underwater object classification using scattering transform of sonar signals

NASA Astrophysics Data System (ADS)

Saito, Naoki; Weber, David S.

2017-08-01

In this paper, we apply the scattering transform (ST)-a nonlinear map based off of a convolutional neural network (CNN)-to classification of underwater objects using sonar signals. The ST formalizes the observation that the filters learned by a CNN have wavelet-like structure. We achieve effective binary classification both on a real dataset of Unexploded Ordinance (UXOs), as well as synthetically generated examples. We also explore the effects on the waveforms with respect to changes in the object domain (e.g., translation, rotation, and acoustic impedance, etc.), and examine the consequences coming from theoretical results for the scattering transform. We show that the scattering transform is capable of excellent classification on both the synthetic and real problems, thanks to having more quasi-invariance properties that are well-suited to translation and rotation of the object.

Plant Classification from Bat-Like Echolocation Signals

PubMed Central

Yovel, Yossi; Franz, Matthias Otto; Stilz, Peter; Schnitzler, Hans-Ulrich

2008-01-01

Classification of plants according to their echoes is an elementary component of bat behavior that plays an important role in spatial orientation and food acquisition. Vegetation echoes are, however, highly complex stochastic signals: from an acoustical point of view, a plant can be thought of as a three-dimensional array of leaves reflecting the emitted bat call. The received echo is therefore a superposition of many reflections. In this work we suggest that the classification of these echoes might not be such a troublesome routine for bats as formerly thought. We present a rather simple approach to classifying signals from a large database of plant echoes that were created by ensonifying plants with a frequency-modulated bat-like ultrasonic pulse. Our algorithm uses the spectrogram of a single echo from which it only uses features that are undoubtedly accessible to bats. We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled. This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis. Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects. PMID:18369425

A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals.

PubMed

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

2014-06-27

Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively. Although the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.

A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals

PubMed Central

2014-01-01

Background Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. Results The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively. Conclusion Although the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database. PMID:24970564

Acoustic Signal Processing in Photorefractive Optical Systems.

NASA Astrophysics Data System (ADS)

Zhou, Gan

This thesis discusses applications of the photorefractive effect in the context of acoustic signal processing. The devices and systems presented here illustrate the ideas and optical principles involved in holographic processing of acoustic information. The interest in optical processing stems from the similarities between holographic optical systems and contemporary models for massively parallel computation, in particular, neural networks. An initial step in acoustic processing is the transformation of acoustic signals into relevant optical forms. A fiber-optic transducer with photorefractive readout transforms acoustic signals into optical images corresponding to their short-time spectrum. The device analyzes complex sound signals and interfaces them with conventional optical correlators. The transducer consists of 130 multimode optical fibers sampling the spectral range of 100 Hz to 5 kHz logarithmically. A physical model of the human cochlea can help us understand some characteristics of human acoustic transduction and signal representation. We construct a life-sized cochlear model using elastic membranes coupled with two fluid-filled chambers, and use a photorefractive novelty filter to investigate its response. The detection sensitivity is determined to be 0.3 angstroms per root Hz at 2 kHz. Qualitative agreement is found between the model response and physiological data. Delay lines map time-domain signals into space -domain and permit holographic processing of temporal information. A parallel optical delay line using dynamic beam coupling in a rotating photorefractive crystal is presented. We experimentally demonstrate a 64 channel device with 0.5 seconds of time-delay and 167 Hz bandwidth. Acoustic signal recognition is described in a photorefractive system implementing the time-delay neural network model. The system consists of a photorefractive optical delay-line and a holographic correlator programmed in a LiNbO_3 crystal. We demonstrate the recognition

Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods.

PubMed

Liu, Boquan; Polce, Evan; Sprott, Julien C; Jiang, Jack J

2018-05-17

The purpose of this study is to introduce a chaos level test to evaluate linear and nonlinear voice type classification method performances under varying signal chaos conditions without subjective impression. Voice signals were constructed with differing degrees of noise to model signal chaos. Within each noise power, 100 Monte Carlo experiments were applied to analyze the output of jitter, shimmer, correlation dimension, and spectrum convergence ratio. The computational output of the 4 classifiers was then plotted against signal chaos level to investigate the performance of these acoustic analysis methods under varying degrees of signal chaos. A diffusive behavior detection-based chaos level test was used to investigate the performances of different voice classification methods. Voice signals were constructed by varying the signal-to-noise ratio to establish differing signal chaos conditions. Chaos level increased sigmoidally with increasing noise power. Jitter and shimmer performed optimally when the chaos level was less than or equal to 0.01, whereas correlation dimension was capable of analyzing signals with chaos levels of less than or equal to 0.0179. Spectrum convergence ratio demonstrated proficiency in analyzing voice signals with all chaos levels investigated in this study. The results of this study corroborate the performance relationships observed in previous studies and, therefore, demonstrate the validity of the validation test method. The presented chaos level validation test could be broadly utilized to evaluate acoustic analysis methods and establish the most appropriate methodology for objective voice analysis in clinical practice.

A Comparison of Signal Enhancement Methods for Extracting Tonal Acoustic Signals

NASA Technical Reports Server (NTRS)

Jones, Michael G.

1998-01-01

The measurement of pure tone acoustic pressure signals in the presence of masking noise, often generated by mean flow, is a continual problem in the field of passive liner duct acoustics research. In support of the Advanced Subsonic Technology Noise Reduction Program, methods were investigated for conducting measurements of advanced duct liner concepts in harsh, aeroacoustic environments. This report presents the results of a comparison study of three signal extraction methods for acquiring quality acoustic pressure measurements in the presence of broadband noise (used to simulate the effects of mean flow). The performance of each method was compared to a baseline measurement of a pure tone acoustic pressure 3 dB above a uniform, broadband noise background.

Acoustic classification of zooplankton

NASA Astrophysics Data System (ADS)

Martin Traykovski, Linda V.

1998-11-01

Work on the forward problem in zooplankton bioacoustics has resulted in the identification of three categories of acoustic scatterers: elastic-shelled (e.g. pteropods), fluid-like (e.g. euphausiids), and gas-bearing (e.g. siphonophores). The relationship between backscattered energy and animal biomass has been shown to vary by a factor of ~19,000 across these categories, so that to make accurate estimates of zooplankton biomass from acoustic backscatter measurements of the ocean, the acoustic characteristics of the species of interest must be well-understood. This thesis describes the development of both feature based and model based classification techniques to invert broadband acoustic echoes from individual zooplankton for scatterer type, as well as for particular parameters such as animal orientation. The feature based Empirical Orthogonal Function Classifier (EOFC) discriminates scatterer types by identifying characteristic modes of variability in the echo spectra, exploiting only the inherent characteristic structure of the acoustic signatures. The model based Model Parameterisation Classifier (MPC) classifies based on correlation of observed echo spectra with simplified parameterisations of theoretical scattering models for the three classes. The Covariance Mean Variance Classifiers (CMVC) are a set of advanced model based techniques which exploit the full complexity of the theoretical models by searching the entire physical model parameter space without employing simplifying parameterisations. Three different CMVC algorithms were developed: the Integrated Score Classifier (ISC), the Pairwise Score Classifier (PSC) and the Bayesian Probability Classifier (BPC); these classifiers assign observations to a class based on similarities in covariance, mean, and variance, while accounting for model ambiguity and validity. These feature based and model based inversion techniques were successfully applied to several thousand echoes acquired from broadband (~350 k

Dimensional analysis of acoustically propagated signals

NASA Technical Reports Server (NTRS)

Hansen, Scott D.; Thomson, Dennis W.

1993-01-01

Traditionally, long term measurements of atmospherically propagated sound signals have consisted of time series of multiminute averages. Only recently have continuous measurements with temporal resolution corresponding to turbulent time scales been available. With modern digital data acquisition systems we now have the capability to simultaneously record both acoustical and meteorological parameters with sufficient temporal resolution to allow us to examine in detail relationships between fluctuating sound and the meteorological variables, particularly wind and temperature, which locally determine the acoustic refractive index. The atmospheric acoustic propagation medium can be treated as a nonlinear dynamical system, a kind of signal processor whose innards depend on thermodynamic and turbulent processes in the atmosphere. The atmosphere is an inherently nonlinear dynamical system. In fact one simple model of atmospheric convection, the Lorenz system, may well be the most widely studied of all dynamical systems. In this paper we report some results of our having applied methods used to characterize nonlinear dynamical systems to study the characteristics of acoustical signals propagated through the atmosphere. For example, we investigate whether or not it is possible to parameterize signal fluctuations in terms of fractal dimensions. For time series one such parameter is the limit capacity dimension. Nicolis and Nicolis were among the first to use the kind of methods we have to study the properties of low dimension global attractors.

Acoustic Localization with Infrasonic Signals

NASA Astrophysics Data System (ADS)

Threatt, Arnesha; Elbing, Brian

2015-11-01

Numerous geophysical and anthropogenic events emit infrasonic frequencies (<20 Hz), including volcanoes, hurricanes, wind turbines and tornadoes. These sounds, which cannot be heard by the human ear, can be detected from large distances (in excess of 100 miles) due to low frequency acoustic signals having a very low decay rate in the atmosphere. Thus infrasound could be used for long-range, passive monitoring and detection of these events. An array of microphones separated by known distances can be used to locate a given source, which is known as acoustic localization. However, acoustic localization with infrasound is particularly challenging due to contamination from other signals, sensitivity to wind noise and producing a trusted source for system development. The objective of the current work is to create an infrasonic source using a propane torch wand or a subwoofer and locate the source using multiple infrasonic microphones. This presentation will present preliminary results from various microphone configurations used to locate the source.

Acoustic transient classification with a template correlation processor.

PubMed

Edwards, R T

1999-10-01

I present an architecture for acoustic pattern classification using trinary-trinary template correlation. In spite of its computational simplicity, the algorithm and architecture represent a method which greatly reduces bandwidth of the input, storage requirements of the classifier memory, and power consumption of the system without compromising classification accuracy. The linear system should be amenable to training using recently-developed methods such as Independent Component Analysis (ICA), and we predict that behavior will be qualitatively similar to that of structures in the auditory cortex.

High temporal resolution of extreme rainfall rate variability and the acoustic classification of rainfall

NASA Astrophysics Data System (ADS)

Nystuen, Jeffrey A.; Amitai, Eyal

2003-04-01

The underwater sound generated by raindrop splashes on a water surface is loud and unique allowing detection, classification and quantification of rainfall. One of the advantages of the acoustic measurement is that the listening area, an effective catchment area, is proportional to the depth of the hydrophone and can be orders of magnitude greater than other in situ rain gauges. This feature allows high temporal resolution of the rainfall measurement. A series of rain events with extremely high rainfall rates, over 100 mm/hr, is examined acoustically. Rapid onset and cessation of rainfall intensity are detected within the convective cells of these storms with maximum 5-s resolution values exceeding 1000 mm/hr. The probability distribution functions (pdf) for rainfall rate occurrence and water volume using the longer temporal resolutions typical of other instruments do not include these extreme values. The variance of sound intensity within different acoustic frequency bands can be used as an aid to classify rainfall type. Objective acoustic classification algorithms are proposed. Within each rainfall classification the relationship between sound intensity and rainfall rate is nearly linear. The reflectivity factor, Z, also has a linear relationship with rainfall rate, R, for each rainfall classification.

Reliable classification of high explosive and chemical/biological artillery using acoustic sensors

NASA Astrophysics Data System (ADS)

Desai, Sachi V.; Hohil, Myron E.; Bass, Henry E.; Chambers, Jim

2005-05-01

Feature extraction methods based on the discrete wavelet transform and multiresolution analysis are used to develop a robust classification algorithm that reliably discriminates between conventional and simulated chemical/biological artillery rounds via acoustic signals produced during detonation utilizing a generic acoustic sensor. Based on the transient properties of the signature blast distinct characteristics arise within the different acoustic signatures because high explosive warheads emphasize concussive and shrapnel effects, while chemical/biological warheads are designed to disperse their contents over large areas, therefore employing a slower burning, less intense explosive to mix and spread their contents. The ensuing blast waves are readily characterized by variations in the corresponding peak pressure and rise time of the blast, differences in the ratio of positive pressure amplitude to the negative amplitude, and variations in the overall duration of the resulting waveform. Unique attributes can also be identified that depend upon the properties of the gun tube, projectile speed at the muzzle, and the explosive burn rates of the warhead. The algorithm enables robust classification of various airburst signatures using acoustics. It is capable of being integrated within an existing chemical/biological sensor, a stand-alone generic sensor, or a part of a disparate sensor suite. When emplaced in high-threat areas, this added capability would further provide field personal with advanced battlefield knowledge without the aide of so-called "sniffer" sensors that rely upon air particle information based on direct contact with possible contaminated air. In this work, the discrete wavelet transform is used to extract the predominant components of these characteristics from air burst signatures at ranges exceeding 2km while maintaining temporal sequence of the data to keep relevance to the transient differences of the airburst signatures. Highly reliable

Intelligent processing of acoustic emission signals

NASA Astrophysics Data System (ADS)

Sachse, Wolfgang; Grabec, Igor

1992-07-01

Recent developments in applying neural-like signal-processing procedures for analyzing acoustic emission signals are summarized. These procedures employ a set of learning signals to develop a memory that can subsequently be utilized to process other signals to recover information about an unknown source. A majority of the current applications to process ultrasonic waveforms are based on multilayered, feed-forward neural networks, trained with some type of back-error propagation rule.

An algorithm of the wildfire classification by its acoustic emission spectrum using Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Khamukhin, A. A.; Demin, A. Y.; Sonkin, D. M.; Bertoldo, S.; Perona, G.; Kretova, V.

2017-01-01

Crown fires are extremely dangerous as the speed of their distribution is dozen times higher compared to surface fires. Therefore, it is important to classify the fire type as early as possible. A method for forest fires classification exploits their computed acoustic emission spectrum compared with a set of samples of the typical fire acoustic emission spectrum stored in the database. This method implies acquisition acoustic data using Wireless Sensors Networks (WSNs) and their analysis in a central processing and a control center. The paper deals with an algorithm which can be directly implemented on a sensor network node that will allow reducing considerably the network traffic and increasing its efficiency. It is hereby suggested to use the sum of the squares ratio, with regard to amplitudes of low and high frequencies of the wildfire acoustic emission spectrum, as the indicator of a forest fire type. It is shown that the value of the crown fires indicator is several times higher than that of the surface ones. This allows classifying the fire types (crown, surface) in a short time interval and transmitting a fire type indicator code alongside with an alarm signal through the network.

Acoustic target detection and classification using neural networks

NASA Technical Reports Server (NTRS)

Robertson, James A.; Conlon, Mark

1993-01-01

A neural network approach to the classification of acoustic emissions of ground vehicles and helicopters is demonstrated. Data collected during the Joint Acoustic Propagation Experiment conducted in July of l991 at White Sands Missile Range, New Mexico was used to train a classifier to distinguish between the spectrums of a UH-1, M60, M1 and M114. An output node was also included that would recognize background (i.e. no target) data. Analysis revealed specific hidden nodes responding to the features input into the classifier. Initial results using the neural network were encouraging with high correct identification rates accompanied by high levels of confidence.

Analysis of signals under compositional noise with applications to SONAR data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tucker, J. Derek; Wu, Wei; Srivastava, Anuj

2013-07-09

In this paper, we consider the problem of denoising and classification of SONAR signals observed under compositional noise, i.e., they have been warped randomly along the x-axis. The traditional techniques do not account for such noise and, consequently, cannot provide a robust classification of signals. We apply a recent framework that: 1) uses a distance-based objective function for data alignment and noise reduction; and 2) leads to warping-invariant distances between signals for robust clustering and classification. We use this framework to introduce two distances that can be used for signal classification: a) a y-distance, which is the distance between themore » aligned signals; and b) an x-distance that measures the amount of warping needed to align the signals. We focus on the task of clustering and classifying objects, using acoustic spectrum (acoustic color), which is complicated by the uncertainties in aspect angles at data collections. Small changes in the aspect angles corrupt signals in a way that amounts to compositional noise. As a result, we demonstrate the use of the developed metrics in classification of acoustic color data and highlight improvements in signal classification over current methods.« less

Perceptual and Acoustic Reliability Estimates for the Speech Disorders Classification System (SDCS)

ERIC Educational Resources Information Center

Shriberg, Lawrence D.; Fourakis, Marios; Hall, Sheryl D.; Karlsson, Heather B.; Lohmeier, Heather L.; McSweeny, Jane L.; Potter, Nancy L.; Scheer-Cohen, Alison R.; Strand, Edythe A.; Tilkens, Christie M.; Wilson, David L.

2010-01-01

A companion paper describes three extensions to a classification system for paediatric speech sound disorders termed the Speech Disorders Classification System (SDCS). The SDCS uses perceptual and acoustic data reduction methods to obtain information on a speaker's speech, prosody, and voice. The present paper provides reliability estimates for…

Classification of underwater targets from autonomous underwater vehicle sampled bistatic acoustic scattered fields.

PubMed

Fischell, Erin M; Schmidt, Henrik

2015-12-01

One of the long term goals of autonomous underwater vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and post-processing and/or image interpretation. A vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target has been developed for onboard, fully autonomous classification with lower cost-per-vehicle. To achieve the high-quality, densely sampled three-dimensional (3D) bistatic scattering data required by this research, vehicle sampling behaviors and an acoustic payload for precision timed data acquisition with a 16 element nose array were demonstrated. 3D bistatic scattered field data were collected by an AUV around spherical and cylindrical targets insonified by a 7-9 kHz fixed source. The collected data were compared to simulated scattering models. Classification and confidence estimation were shown for the sphere versus cylinder case on the resulting real and simulated bistatic amplitude data. The final models were used for classification of simulated targets in real time in the LAMSS MOOS-IvP simulation package [M. Benjamin, H. Schmidt, P. Newman, and J. Leonard, J. Field Rob. 27, 834-875 (2010)].

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

Signal Restoration of Non-stationary Acoustic Signals in the Time Domain

NASA Technical Reports Server (NTRS)

Babkin, Alexander S.

1988-01-01

Signal restoration is a method of transforming a nonstationary signal acquired by a ground based microphone to an equivalent stationary signal. The benefit of the signal restoration is a simplification of the flight test requirements because it could dispense with the need to acquire acoustic data with another aircraft flying in concert with the rotorcraft. The data quality is also generally improved because the contamination of the signal by the propeller and wind noise is not present. The restoration methodology can also be combined with other data acquisition methods, such as a multiple linear microphone array for further improvement of the test results. The methodology and software are presented for performing the signal restoration in the time domain. The method has no restrictions on flight path geometry or flight regimes. Only requirement is that the aircraft spatial position be known relative to the microphone location and synchronized with the acoustic data. The restoration process assumes that the moving source radiates a stationary signal, which is then transformed into a nonstationary signal by various modulation processes. The restoration contains only the modulation due to the source motion.

Signal classification using global dynamical models, Part II: SONAR data analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kremliovsky, M.; Kadtke, J.

1996-06-01

In Part I of this paper, we described a numerical method for nonlinear signal detection and classification which made use of techniques borrowed from dynamical systems theory. Here in Part II of the paper, we will describe an example of data analysis using this method, for data consisting of open ocean acoustic (SONAR) recordings of marine mammal transients, supplied from NUWC sources. The purpose here is two-fold: first to give a more operational description of the technique and provide rules-of-thumb for parameter choices; and second to discuss some new issues raised by the analysis of non-ideal (real-world) data sets. Themore » particular data set considered here is quite non-stationary, relatively noisy, is not clearly localized in the background, and as such provides a difficult challenge for most detection/classification schemes. {copyright} {ital 1996 American Institute of Physics.}« less

Acoustic Event Detection and Classification

NASA Astrophysics Data System (ADS)

Temko, Andrey; Nadeu, Climent; Macho, Dušan; Malkin, Robert; Zieger, Christian; Omologo, Maurizio

The human activity that takes place in meeting rooms or classrooms is reflected in a rich variety of acoustic events (AE), produced either by the human body or by objects handled by humans, so the determination of both the identity of sounds and their position in time may help to detect and describe that human activity. Indeed, speech is usually the most informative sound, but other kinds of AEs may also carry useful information, for example, clapping or laughing inside a speech, a strong yawn in the middle of a lecture, a chair moving or a door slam when the meeting has just started. Additionally, detection and classification of sounds other than speech may be useful to enhance the robustness of speech technologies like automatic speech recognition.

PREDICTIVE MODELING OF ACOUSTIC SIGNALS FROM THERMOACOUSTIC POWER SENSORS (TAPS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dumm, Christopher M.; Vipperman, Jeffrey S.

2016-06-30

Thermoacoustic Power Sensor (TAPS) technology offers the potential for self-powered, wireless measurement of nuclear reactor core operating conditions. TAPS are based on thermoacoustic engines, which harness thermal energy from fission reactions to generate acoustic waves by virtue of gas motion through a porous stack of thermally nonconductive material. TAPS can be placed in the core, where they generate acoustic waves whose frequency and amplitude are proportional to the local temperature and radiation flux, respectively. TAPS acoustic signals are not measured directly at the TAPS; rather, they propagate wirelessly from an individual TAPS through the reactor, and ultimately to a low-powermore » receiver network on the vessel’s exterior. In order to rely on TAPS as primary instrumentation, reactor-specific models which account for geometric/acoustic complexities in the signal propagation environment must be used to predict the amplitude and frequency of TAPS signals at receiver locations. The reactor state may then be derived by comparing receiver signals to the reference levels established by predictive modeling. In this paper, we develop and experimentally benchmark a methodology for predictive modeling of the signals generated by a TAPS system, with the intent of subsequently extending these efforts to modeling of TAPS in a liquid sodium environmen« less

Comparison of different classification algorithms for underwater target discrimination.

PubMed

Li, Donghui; Azimi-Sadjadi, Mahmood R; Robinson, Marc

2004-01-01

Classification of underwater targets from the acoustic backscattered signals is considered here. Several different classification algorithms are tested and benchmarked not only for their performance but also to gain insight to the properties of the feature space. Results on a wideband 80-kHz acoustic backscattered data set collected for six different objects are presented in terms of the receiver operating characteristic (ROC) and robustness of the classifiers wrt reverberation.

Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water

NASA Astrophysics Data System (ADS)

Li, Dong; Tang, Cheng; Xia, Chunlei; Zhang, Hua

2017-02-01

Artificial reefs (ARs) are effective means to maintain fishery resources and to restore ecological environment in coastal waters. ARs have been widely constructed along the Chinese coast. However, understanding of benthic habitats in the vicinity of ARs is limited, hindering effective fisheries and aquacultural management. Multibeam echosounder (MBES) is an advanced acoustic instrument capable of efficiently generating large-scale maps of benthic environments at fine resolutions. The objective of this study is to develop a technical approach to characterize, classify, and map shallow coastal areas with ARs using an MBES. An automated classification method is designed and tested to process bathymetric and backscatter data from MBES and transform the variables into simple, easily visualized maps. To reduce the redundancy in acoustic variables, a principal component analysis (PCA) is used to condense the highly collinear dataset. An acoustic benthic map of bottom sediments is classified using an iterative self-organizing data analysis technique (ISODATA). The approach is tested with MBES surveys in a 1.15 km2 fish farm with a high density of ARs off the Yantai coast in northern China. Using this method, 3 basic benthic habitats (sandy bottom, muddy sediments, and ARs) are distinguished. The results of the classification are validated using sediment samples and underwater surveys. Our study shows that the use of MBES is an effective method for acoustic mapping and classification of ARs.

Primary acoustic signal structure during free falling drop collision with a water surface

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chashechkin, Yu. D., E-mail: chakin@ipmnet.ru; Prokhorov, V. E., E-mail: prohorov@ipmnet.ru

2016-04-15

Consistent optical and acoustic techniques have been used to study the structure of hydrodynamic disturbances and acoustic signals generated as a free falling drop penetrates water. The relationship between the structures of hydrodynamic and acoustic perturbations arising as a result of a falling drop contacting with the water surface and subsequent immersion into water is traced. The primary acoustic signal is characterized, in addition to stably reproduced features (steep leading edge followed by long decay with local pressure maxima), by irregular high-frequency packets, which are studied for the first time. Reproducible experimental data are used to recognize constant and variablemore » components of the primary acoustic signal.« less

Prediction of acoustic feature parameters using myoelectric signals.

PubMed

Lee, Ki-Seung

2010-07-01

It is well-known that a clear relationship exists between human voices and myoelectric signals (MESs) from the area of the speaker's mouth. In this study, we utilized this information to implement a speech synthesis scheme in which MES alone was used to predict the parameters characterizing the vocal-tract transfer function of specific speech signals. Several feature parameters derived from MES were investigated to find the optimal feature for maximization of the mutual information between the acoustic and the MES features. After the optimal feature was determined, an estimation rule for the acoustic parameters was proposed, based on a minimum mean square error (MMSE) criterion. In a preliminary study, 60 isolated words were used for both objective and subjective evaluations. The results showed that the average Euclidean distance between the original and predicted acoustic parameters was reduced by about 30% compared with the average Euclidean distance of the original parameters. The intelligibility of the synthesized speech signals using the predicted features was also evaluated. A word-level identification ratio of 65.5% and a syllable-level identification ratio of 73% were obtained through a listening test.

Modeling of acoustic emission signal propagation in waveguides.

PubMed

Zelenyak, Andreea-Manuela; Hamstad, Marvin A; Sause, Markus G R

2015-05-21

Acoustic emission (AE) testing is a widely used nondestructive testing (NDT) method to investigate material failure. When environmental conditions are harmful for the operation of the sensors, waveguides are typically mounted in between the inspected structure and the sensor. Such waveguides can be built from different materials or have different designs in accordance with the experimental needs. All these variations can cause changes in the acoustic emission signals in terms of modal conversion, additional attenuation or shift in frequency content. A finite element method (FEM) was used to model acoustic emission signal propagation in an aluminum plate with an attached waveguide and was validated against experimental data. The geometry of the waveguide is systematically changed by varying the radius and height to investigate the influence on the detected signals. Different waveguide materials were implemented and change of material properties as function of temperature were taken into account. Development of the option of modeling different waveguide options replaces the time consuming and expensive trial and error alternative of experiments. Thus, the aim of this research has important implications for those who use waveguides for AE testing.

Identification of Damaged Wheat Kernels and Cracked-Shell Hazelnuts with Impact Acoustics Time-Frequency Patterns

USDA-ARS?s Scientific Manuscript database

A new adaptive time-frequency (t-f) analysis and classification procedure is applied to impact acoustic signals for detecting hazelnuts with cracked shells and three types of damaged wheat kernels. Kernels were dropped onto a steel plate, and the resulting impact acoustic signals were recorded with ...

The effect of habitat acoustics on common marmoset vocal signal transmission.

PubMed

Morrill, Ryan J; Thomas, A Wren; Schiel, Nicola; Souto, Antonio; Miller, Cory T

2013-09-01

Noisy acoustic environments present several challenges for the evolution of acoustic communication systems. Among the most significant is the need to limit degradation of spectro-temporal signal structure in order to maintain communicative efficacy. This can be achieved by selecting for several potentially complementary processes. Selection can act on behavioral mechanisms permitting signalers to control the timing and occurrence of signal production to avoid acoustic interference. Likewise, the signal itself may be the target of selection, biasing the evolution of its structure to comprise acoustic features that avoid interference from ambient noise or degrade minimally in the habitat. Here, we address the latter topic for common marmoset (Callithrix jacchus) long-distance contact vocalizations, known as phee calls. Our aim was to test whether this vocalization is specifically adapted for transmission in a species-typical forest habitat, the Atlantic forests of northeastern Brazil. We combined seasonal analyses of ambient habitat acoustics with experiments in which pure tones, clicks, and vocalizations were broadcast and rerecorded at different distances to characterize signal degradation in the habitat. Ambient sound was analyzed from intervals throughout the day and over rainy and dry seasons, showing temporal regularities across varied timescales. Broadcast experiment results indicated that the tone and click stimuli showed the typically inverse relationship between frequency and signaling efficacy. Although marmoset phee calls degraded over distance with marked predictability compared with artificial sounds, they did not otherwise appear to be specially designed for increased transmission efficacy or minimal interference in this habitat. We discuss these data in the context of other similar studies and evidence of potential behavioral mechanisms for avoiding acoustic interference in order to maintain effective vocal communication in common marmosets. © 2013

The Effect of Habitat Acoustics on Common Marmoset Vocal Signal Transmission

PubMed Central

MORRILL, RYAN J.; THOMAS, A. WREN; SCHIEL, NICOLA; SOUTO, ANTONIO; MILLER, CORY T.

2013-01-01

Noisy acoustic environments present several challenges for the evolution of acoustic communication systems. Among the most significant is the need to limit degradation of spectro-temporal signal structure in order to maintain communicative efficacy. This can be achieved by selecting for several potentially complementary processes. Selection can act on behavioral mechanisms permitting signalers to control the timing and occurrence of signal production to avoid acoustic interference. Likewise, the signal itself may be the target of selection, biasing the evolution of its structure to comprise acoustic features that avoid interference from ambient noise or degrade minimally in the habitat. Here, we address the latter topic for common marmoset (Callithrix jacchus) long-distance contact vocalizations, known as phee calls. Our aim was to test whether this vocalization is specifically adapted for transmission in a species-typical forest habitat, the Atlantic forests of northeastern Brazil. We combined seasonal analyses of ambient habitat acoustics with experiments in which pure tones, clicks, and vocalizations were broadcast and rerecorded at different distances to characterize signal degradation in the habitat. Ambient sound was analyzed from intervals throughout the day and over rainy and dry seasons, showing temporal regularities across varied timescales. Broadcast experiment results indicated that the tone and click stimuli showed the typically inverse relationship between frequency and signaling efficacy. Although marmoset phee calls degraded over distance with marked predictability compared with artificial sounds, they did not otherwise appear to be specially designed for increased transmission efficacy or minimal interference in this habitat. We discuss these data in the context of other similar studies and evidence of potential behavioral mechanisms for avoiding acoustic interference in order to maintain effective vocal communication in common marmosets. PMID

Inversion of High Frequency Acoustic Data for Sediment Properties Needed for the Detection and Classification of UXOs

DTIC Science & Technology

2015-05-26

FINAL REPORT Inversion of High Frequency Acoustic Data for Sediment Properties Needed for the Detection and Classification of UXOs SERDP...DOCUMENTATION PAGE Prescribed by ANSI Std. Z39.18 Form Approved OMB No. 0704-0188 The public reporting burden for this collection of information is...2015 Inversion of High Frequency Acoustic Data for Sediment Properties Needed for the Detection and Classification of UXO’s W912HQ-12-C-0049 MR

Intense acoustic bursts as a signal-enhancement mechanism in ultrasound-modulated optical tomography.

PubMed

Kim, Chulhong; Zemp, Roger J; Wang, Lihong V

2006-08-15

Biophotonic imaging with ultrasound-modulated optical tomography (UOT) promises ultrasonically resolved imaging in biological tissues. A key challenge in this imaging technique is a low signal-to-noise ratio (SNR). We show significant UOT signal enhancement by using intense time-gated acoustic bursts. A CCD camera captured the speckle pattern from a laser-illuminated tissue phantom. Differences in speckle contrast were observed when ultrasonic bursts were applied, compared with when no ultrasound was applied. When CCD triggering was synchronized with burst initiation, acoustic-radiation-force-induced displacements were detected. To avoid mechanical contrast in UOT images, the CCD camera acquisition was delayed several milliseconds until transient effects of acoustic radiation force attenuated to a satisfactory level. The SNR of our system was sufficiently high to provide an image pixel per acoustic burst without signal averaging. Because of the substantially improved SNR, the use of intense acoustic bursts is a promising signal enhancement strategy for UOT.

Joint deconvolution and classification with applications to passive acoustic underwater multipath.

PubMed

Anderson, Hyrum S; Gupta, Maya R

2008-11-01

This paper addresses the problem of classifying signals that have been corrupted by noise and unknown linear time-invariant (LTI) filtering such as multipath, given labeled uncorrupted training signals. A maximum a posteriori approach to the deconvolution and classification is considered, which produces estimates of the desired signal, the unknown channel, and the class label. For cases in which only a class label is needed, the classification accuracy can be improved by not committing to an estimate of the channel or signal. A variant of the quadratic discriminant analysis (QDA) classifier is proposed that probabilistically accounts for the unknown LTI filtering, and which avoids deconvolution. The proposed QDA classifier can work either directly on the signal or on features whose transformation by LTI filtering can be analyzed; as an example a classifier for subband-power features is derived. Results on simulated data and real Bowhead whale vocalizations show that jointly considering deconvolution with classification can dramatically improve classification performance over traditional methods over a range of signal-to-noise ratios.

Characterizing, synthesizing, and/or canceling out acoustic signals from sound sources

DOEpatents

Holzrichter, John F [Berkeley, CA; Ng, Lawrence C [Danville, CA

2007-03-13

A system for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate and animate sound sources. Electromagnetic sensors monitor excitation sources in sound producing systems, such as animate sound sources such as the human voice, or from machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The systems disclosed enable accurate calculation of transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems

DTIC Science & Technology

2016-08-05

JPAnalytics LLC CC: DCMA Boston DTIC Director, NRL Progress Report #8 Coupled Research in Ocean Acoustics and Signal Processing for the Next...Generation of Underwater Acoustic Communication Systems Principal Investigator’s Name: Dr. James Preisig Period Covered By Report: 1/20/2016 to 4/19/2016...Technical work this period has spanned two areas. The first of these is VHF Acoustics . During this time period, the Principle Investigator worked with Dr

Investigation of Volcanic Seismo-Acoustic Signals: Applying Subspace Detection to Lava Fountain Activity at Etna Volcano

NASA Astrophysics Data System (ADS)

Sciotto, M.; Rowe, C. A.; Cannata, A.; Arrowsmith, S.; Privitera, E.; Gresta, S.

2011-12-01

The current eruption of Mount Etna, which began in January, 2011, has produced numerous energetic episodes of lava fountaining, which have bee recorded by the INGV seismic and acoustic sensors located on and around the volcano. The source of these events was the pit crater on the east flank of the Southeast crater of Etna. Simultaneously, small levels of activity were noted in the Bocca Nuova as well, prior to its lava fountaining activity. We will present an analysis of seismic and acoustic signals related to the 2011 activity wherein we apply the method of subspace detection to determine whether the source exhibits a temporal evolution within or between fountaining events, or otherwise produces repeating, classifiable events occurring through the continuous explosive degassing. We will examine not only the raw waveforms, but also spectral variations in time as well as time-varying statistical functions such as signal skewness and kurtosis. These results will be compared to straightforward cross-correlation analysis. In addition to classification performance, the subspace method has promise to outperform standard STA/LTA methods for real-time event detection in cases where similar events can be expected.

Acoustic/seismic signal propagation and sensor performance modeling

NASA Astrophysics Data System (ADS)

Wilson, D. Keith; Marlin, David H.; Mackay, Sean

2007-04-01

Performance, optimal employment, and interpretation of data from acoustic and seismic sensors depend strongly and in complex ways on the environment in which they operate. Software tools for guiding non-expert users of acoustic and seismic sensors are therefore much needed. However, such tools require that many individual components be constructed and correctly connected together. These components include the source signature and directionality, representation of the atmospheric and terrain environment, calculation of the signal propagation, characterization of the sensor response, and mimicking of the data processing at the sensor. Selection of an appropriate signal propagation model is particularly important, as there are significant trade-offs between output fidelity and computation speed. Attenuation of signal energy, random fading, and (for array systems) variations in wavefront angle-of-arrival should all be considered. Characterization of the complex operational environment is often the weak link in sensor modeling: important issues for acoustic and seismic modeling activities include the temporal/spatial resolution of the atmospheric data, knowledge of the surface and subsurface terrain properties, and representation of ambient background noise and vibrations. Design of software tools that address these challenges is illustrated with two examples: a detailed target-to-sensor calculation application called the Sensor Performance Evaluator for Battlefield Environments (SPEBE) and a GIS-embedded approach called Battlefield Terrain Reasoning and Awareness (BTRA).

System and method for investigating sub-surface features of a rock formation with acoustic sources generating coded signals

DOEpatents

Vu, Cung Khac; Nihei, Kurt; Johnson, Paul A; Guyer, Robert; Ten Cate, James A; Le Bas, Pierre-Yves; Larmat, Carene S

2014-12-30

A system and a method for investigating rock formations includes generating, by a first acoustic source, a first acoustic signal comprising a first plurality of pulses, each pulse including a first modulated signal at a central frequency; and generating, by a second acoustic source, a second acoustic signal comprising a second plurality of pulses. A receiver arranged within the borehole receives a detected signal including a signal being generated by a non-linear mixing process from the first-and-second acoustic signal in a non-linear mixing zone within the intersection volume. The method also includes-processing the received signal to extract the signal generated by the non-linear mixing process over noise or over signals generated by a linear interaction process, or both.

Applying the Multiple Signal Classification Method to Silent Object Detection Using Ambient Noise

NASA Astrophysics Data System (ADS)

Mori, Kazuyoshi; Yokoyama, Tomoki; Hasegawa, Akio; Matsuda, Minoru

2004-05-01

The revolutionary concept of using ocean ambient noise positively to detect objects, called acoustic daylight imaging, has attracted much attention. The authors attempted the detection of a silent target object using ambient noise and a wide-band beam former consisting of an array of receivers. In experimental results obtained in air, using the wide-band beam former, we successfully applied the delay-sum array (DSA) method to detect a silent target object in an acoustic noise field generated by a large number of transducers. This paper reports some experimental results obtained by applying the multiple signal classification (MUSIC) method to a wide-band beam former to detect silent targets. The ocean ambient noise was simulated by transducers decentralized to many points in air. Both MUSIC and DSA detected a spherical target object in the noise field. The relative power levels near the target obtained with MUSIC were compared with those obtained by DSA. Then the effectiveness of the MUSIC method was evaluated according to the rate of increase in the maximum and minimum relative power levels.

Bird population density estimated from acoustic signals

USGS Publications Warehouse

Dawson, D.K.; Efford, M.G.

2009-01-01

Many animal species are detected primarily by sound. Although songs, calls and other sounds are often used for population assessment, as in bird point counts and hydrophone surveys of cetaceans, there are few rigorous methods for estimating population density from acoustic data. 2. The problem has several parts - distinguishing individuals, adjusting for individuals that are missed, and adjusting for the area sampled. Spatially explicit capture-recapture (SECR) is a statistical methodology that addresses jointly the second and third parts of the problem. We have extended SECR to use uncalibrated information from acoustic signals on the distance to each source. 3. We applied this extension of SECR to data from an acoustic survey of ovenbird Seiurus aurocapilla density in an eastern US deciduous forest with multiple four-microphone arrays. We modelled average power from spectrograms of ovenbird songs measured within a window of 0??7 s duration and frequencies between 4200 and 5200 Hz. 4. The resulting estimates of the density of singing males (0??19 ha -1 SE 0??03 ha-1) were consistent with estimates of the adult male population density from mist-netting (0??36 ha-1 SE 0??12 ha-1). The fitted model predicts sound attenuation of 0??11 dB m-1 (SE 0??01 dB m-1) in excess of losses from spherical spreading. 5.Synthesis and applications. Our method for estimating animal population density from acoustic signals fills a gap in the census methods available for visually cryptic but vocal taxa, including many species of bird and cetacean. The necessary equipment is simple and readily available; as few as two microphones may provide adequate estimates, given spatial replication. The method requires that individuals detected at the same place are acoustically distinguishable and all individuals vocalize during the recording interval, or that the per capita rate of vocalization is known. We believe these requirements can be met, with suitable field methods, for a significant

A real-time method for autonomous passive acoustic detection-classification of humpback whales.

PubMed

Abbot, Ted A; Premus, Vincent E; Abbot, Philip A

2010-05-01

This paper describes a method for real-time, autonomous, joint detection-classification of humpback whale vocalizations. The approach adapts the spectrogram correlation method used by Mellinger and Clark [J. Acoust. Soc. Am. 107, 3518-3529 (2000)] for bowhead whale endnote detection to the humpback whale problem. The objective is the implementation of a system to determine the presence or absence of humpback whales with passive acoustic methods and to perform this classification with low false alarm rate in real time. Multiple correlation kernels are used due to the diversity of humpback song. The approach also takes advantage of the fact that humpbacks tend to vocalize repeatedly for extended periods of time, and identification is declared only when multiple song units are detected within a fixed time interval. Humpback whale vocalizations from Alaska, Hawaii, and Stellwagen Bank were used to train the algorithm. It was then tested on independent data obtained off Kaena Point, Hawaii in February and March of 2009. Results show that the algorithm successfully classified humpback whales autonomously in real time, with a measured probability of correct classification in excess of 74% and a measured probability of false alarm below 1%.

Digital Signal Processing in Acoustics--Part 2.

ERIC Educational Resources Information Center

Davies, H.; McNeill, D. J.

1986-01-01

Reviews the potential of a data acquisition system for illustrating the nature and significance of ideas in digital signal processing. Focuses on the fast Fourier transform and the utility of its two-channel format, emphasizing cross-correlation and its two-microphone technique of acoustic intensity measurement. Includes programing format. (ML)

Terrain-Moisture Classification Using GPS Surface-Reflected Signals

NASA Technical Reports Server (NTRS)

Grant, Michael S.; Acton, Scott T.; Katzberg, Stephen J.

2006-01-01

In this study we present a novel method of land surface classification using surface-reflected GPS signals in combination with digital imagery. Two GPS-derived classification features are merged with visible image data to create terrain-moisture (TM) classes, defined here as visibly identifiable terrain or landcover classes containing a surface/soil moisture component. As compared to using surface imagery alone, classification accuracy is significantly improved for a number of visible classes when adding the GPS-based signal features. Since the strength of the reflected GPS signal is proportional to the amount of moisture in the surface, use of these GPS features provides information about the surface that is not obtainable using visible wavelengths alone. Application areas include hydrology, precision agriculture, and wetlands mapping.

Real-Time Speech/Music Classification With a Hierarchical Oblique Decision Tree

DTIC Science & Technology

2008-04-01

REAL-TIME SPEECH/ MUSIC CLASSIFICATION WITH A HIERARCHICAL OBLIQUE DECISION TREE Jun Wang, Qiong Wu, Haojiang Deng, Qin Yan Institute of Acoustics...time speech/ music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected...handle signals without discrimination and can not work properly in the existence of multimedia signals. This paper proposes a real-time speech/ music

A comparative evaluation of piezoelectric sensors for acoustic emission-based impact location estimation and damage classification in composite structures

NASA Astrophysics Data System (ADS)

Uprety, Bibhisha; Kim, Sungwon; Mathews, V. John; Adams, Daniel O.

2015-03-01

Acoustic Emission (AE) based Structural Health Monitoring (SHM) is of great interest for detecting impact damage in composite structures. Within the aerospace industry the need to detect and locate these events, even when no visible damage is present, is important both from the maintenance and design perspectives. In this investigation, four commercially available piezoelectric sensors were evaluated for usage in an AE-based SHM system. Of particular interest was comparing the acoustic response of the candidate piezoelectric sensors for impact location estimations as well as damage classification resulting from the impact in fiber-reinforced composite structures. Sensor assessment was performed based on response signal characterization and performance for active testing at 300 kHz and steel-ball drop testing using both aluminum and carbon/epoxy composite plates. Wave mode velocities calculated from the measured arrival times were found to be in good agreement with predictions obtained using both the Disperse code and finite element analysis. Differences in the relative strength of the received wave modes, the overall signal strengths and signal-to-noise ratios were observed through the use of both active testing as well as passive steel-ball drop testing. Further comparative is focusing on assessing AE sensor performance for use in impact location estimation algorithms as well as detecting and classifying damage produced in composite structures due to impact events.

Continental-shelf Scale Passive Ocean AcousticWaveguide Remote Sensing of Marine Mammals and other Submerged Objects including Detection, Localization, and Classification

NASA Astrophysics Data System (ADS)

Wang, Delin

In this thesis, we develop the basics of the Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) technique for the instantaneous continental-shelf scale detection, localization and species classification of marine mammal vocalizations. POAWRS uses a large-aperture, densely sampled coherent hydrophone array system with orders of magnitude higher array gain to enhance signal-to-noise ratios (SNR) by coherent beamforming, enabling detection of underwater acoustic signals either two orders of magnitude more distant in range or lower in SNR than a single hydrophone. The ability to employ coherent spatial processing of signals with the POAWRS technology significantly improves areal coverage, enabling detection of oceanic sound sources over instantaneous wide areas spanning 100 km or more in diameter. The POAWRS approach was applied to analyze marine mammal vocalizations from diverse species received on a 160-element Office Naval Research Five Octave Research Array (ONR-FORA) deployed during their feeding season in Fall 2006 in the Gulf of Maine. The species-dependent temporal-spatial distribution of marine mammal vocalizations and correlation to the prey fish distributions have been determined. Furthermore, the probability of detection regions, source level distributions and pulse compression gains of the vocalization signals from diverse marine mammal species have been estimated. We also develop an approach for enhancing the angular resolution and improving bearing estimates of acoustic signals received on a coherent hydrophone array with multiple-nested uniformly-spaced subapertures, such as the ONR-FORA, by nonuniform array beamforming. Finally we develop a low-cost non-oil-filled towable prototype hydrophone array that consists of eight hydrophone elements with real-time data acquisition and 100 m tow cable. The approach demonstrated here will be applied in the development of a full 160 element POAWRS-type low-cost coherent hydrophone array system in the future.

Workshop on the Detection, Classification, Localization and Density Estimation of Marine Mammals Using Passive Acoustics - 2015

DTIC Science & Technology

2015-09-30

together the research community working on marine mammal acoustics to discuss detection, classification, localization and density estimation methods...and Density Estimation of Marine Mammals Using Passive Acoustics - 2015 John A. Hildebrand Scripps Institution of Oceanography UCSD La Jolla...dclde LONG-TERM GOALS The goal of this project was to bring together the community of researchers working on methods for detection

Subsurface event detection and classification using Wireless Signal Networks.

PubMed

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

2012-11-05

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

Real-time, in situ monitoring of nanoporation using electric field-induced acoustic signal

NASA Astrophysics Data System (ADS)

Zarafshani, Ali; Faiz, Rowzat; Samant, Pratik; Zheng, Bin; Xiang, Liangzhong

2018-02-01

The use of nanoporation in reversible or irreversible electroporation, e.g. cancer ablation, is rapidly growing. This technique uses an ultra-short and intense electric pulse to increase the membrane permeability, allowing non-permeant drugs and genes access to the cytosol via nanopores in the plasma membrane. It is vital to create a real-time in situ monitoring technique to characterize this process and answer the need created by the successful electroporation procedure of cancer treatment. All suggested monitoring techniques for electroporation currently are for pre-and post-stimulation exposure with no real-time monitoring during electric field exposure. This study was aimed at developing an innovative technology for real-time in situ monitoring of electroporation based on the typical cell exposure-induced acoustic emissions. The acoustic signals are the result of the electric field, which itself can be used in realtime to characterize the process of electroporation. We varied electric field distribution by varying the electric pulse from 1μ - 100ns and varying the voltage intensity from 0 - 1.2ܸ݇ to energize two electrodes in a bi-polar set-up. An ultrasound transducer was used for collecting acoustic signals around the subject under test. We determined the relative location of the acoustic signals by varying the position of the electrodes relative to the transducer and varying the electric field distribution between the electrodes to capture a variety of acoustic signals. Therefore, the electric field that is utilized in the nanoporation technique also produces a series of corresponding acoustic signals. This offers a novel imaging technique for the real-time in situ monitoring of electroporation that may directly improve treatment efficiency.

Impact of the Test Device on the Behavior of the Acoustic Emission Signals: Contribution of the Numerical Modeling to Signal Processing

NASA Astrophysics Data System (ADS)

Issiaka Traore, Oumar; Cristini, Paul; Favretto-Cristini, Nathalie; Pantera, Laurent; Viguier-Pla, Sylvie

2018-01-01

In a context of nuclear safety experiment monitoring with the non destructive testing method of acoustic emission, we study the impact of the test device on the interpretation of the recorded physical signals by using spectral finite element modeling. The numerical results are validated by comparison with real acoustic emission data obtained from previous experiments. The results show that several parameters can have significant impacts on acoustic wave propagation and then on the interpretation of the physical signals. The potential position of the source mechanism, the positions of the receivers and the nature of the coolant fluid have to be taken into account in the definition a pre-processing strategy of the real acoustic emission signals. In order to show the relevance of such an approach, we use the results to propose an optimization of the positions of the acoustic emission sensors in order to reduce the estimation bias of the time-delay and then improve the localization of the source mechanisms.

Acoustic source signal and directivity for explosive sources in complex environments

NASA Astrophysics Data System (ADS)

Waxler, R.; Bonner, J. L.; Reinke, R.; Talmadge, C. L.; Kleinert, D. E.; Alberts, W.; Lennox, E.

2012-12-01

Much work has gone into characterizing the blast wave, and ultimate acoustic pulse, produced by an explosion in flat, open land. Recently, an experiment was performed to study signals produced by explosions in more complex environments, both above and below ground. Explosive charges, ranging in weight from 200 to 2000 lbs., were detonated in a variety of configurations in and around tubes and culverts as well as buried in alluvium and limestone. A large number of acoustic sensors were deployed to capture the signals from the explosions. The deployment included two concentric rings of eighteen sensors each, spaced roughly every twenty degrees at radii of 300 and 1000 meters and surrounding the explosions. These captured the acoustic source function and directivity. In addition, a network of sensors, including sensors mounted on an aerostat and elevated to 300 meters altitude, were deployed throughout the area to capture the signals as they propagated. The meteorological state was monitored with a variety of instruments including a tethersonde, radiosonde and sodar. Significant directivity was observed in the signals from many of the shots, including those from charges that were detonated underground, but not near any structure. Results from the experiment will be presented.

Call transmission efficiency in native and invasive anurans: competing hypotheses of divergence in acoustic signals.

PubMed

Llusia, Diego; Gómez, Miguel; Penna, Mario; Márquez, Rafael

2013-01-01

Invasive species are a leading cause of the current biodiversity decline, and hence examining the major traits favouring invasion is a key and long-standing goal of invasion biology. Despite the prominent role of the advertisement calls in sexual selection and reproduction, very little attention has been paid to the features of acoustic communication of invasive species in nonindigenous habitats and their potential impacts on native species. Here we compare for the first time the transmission efficiency of the advertisement calls of native and invasive species, searching for competitive advantages for acoustic communication and reproduction of introduced taxa, and providing insights into competing hypotheses in evolutionary divergence of acoustic signals: acoustic adaptation vs. morphological constraints. Using sound propagation experiments, we measured the attenuation rates of pure tones (0.2-5 kHz) and playback calls (Lithobates catesbeianus and Pelophylax perezi) across four distances (1, 2, 4, and 8 m) and over two substrates (water and soil) in seven Iberian localities. All factors considered (signal type, distance, substrate, and locality) affected transmission efficiency of acoustic signals, which was maximized with lower frequency sounds, shorter distances, and over water surface. Despite being broadcast in nonindigenous habitats, the advertisement calls of invasive L. catesbeianus were propagated more efficiently than those of the native species, in both aquatic and terrestrial substrates, and in most of the study sites. This implies absence of optimal relationship between native environments and propagation of acoustic signals in anurans, in contrast to what predicted by the acoustic adaptation hypothesis, and it might render these vertebrates particularly vulnerable to intrusion of invasive species producing low frequency signals, such as L. catesbeianus. Our findings suggest that mechanisms optimizing sound transmission in native habitat can play a less

Call Transmission Efficiency in Native and Invasive Anurans: Competing Hypotheses of Divergence in Acoustic Signals

PubMed Central

Llusia, Diego; Gómez, Miguel; Penna, Mario; Márquez, Rafael

2013-01-01

Invasive species are a leading cause of the current biodiversity decline, and hence examining the major traits favouring invasion is a key and long-standing goal of invasion biology. Despite the prominent role of the advertisement calls in sexual selection and reproduction, very little attention has been paid to the features of acoustic communication of invasive species in nonindigenous habitats and their potential impacts on native species. Here we compare for the first time the transmission efficiency of the advertisement calls of native and invasive species, searching for competitive advantages for acoustic communication and reproduction of introduced taxa, and providing insights into competing hypotheses in evolutionary divergence of acoustic signals: acoustic adaptation vs. morphological constraints. Using sound propagation experiments, we measured the attenuation rates of pure tones (0.2–5 kHz) and playback calls (Lithobates catesbeianus and Pelophylax perezi) across four distances (1, 2, 4, and 8 m) and over two substrates (water and soil) in seven Iberian localities. All factors considered (signal type, distance, substrate, and locality) affected transmission efficiency of acoustic signals, which was maximized with lower frequency sounds, shorter distances, and over water surface. Despite being broadcast in nonindigenous habitats, the advertisement calls of invasive L. catesbeianus were propagated more efficiently than those of the native species, in both aquatic and terrestrial substrates, and in most of the study sites. This implies absence of optimal relationship between native environments and propagation of acoustic signals in anurans, in contrast to what predicted by the acoustic adaptation hypothesis, and it might render these vertebrates particularly vulnerable to intrusion of invasive species producing low frequency signals, such as L. catesbeianus. Our findings suggest that mechanisms optimizing sound transmission in native habitat can play a

Amplitude calibration of an acoustic backscattered signal from a bottom-moored ADCP based on long-term measurement series

NASA Astrophysics Data System (ADS)

Piotukh, V. B.; Zatsepin, A. G.; Kuklev, S. B.

2017-05-01

A possible approach to, and preliminary results of, amplitude calibration of acoustic signals backscattered from an ADCP moored at the bottom of the near-shelf zone of the Black Sea is considered. The aim of this work is to obtain vertical profiles of acoustic scattering signal levels, showing the real characteristics of the volume content of suspended sediments in sea water in units of conventional acoustic turbidity for a given signal frequency. In this case, the assumption about the intervals of maximum acoustic transparency and vertical homogeneity of the marine environment in long-term series of ADCP measurements is used. According to this hypothesis, the intervals of the least values of acoustic backscattered signals are detected, an empirical transfer function of the ADCP reception path is constructed, and it is calibrated. Normalized sets of acoustic backscattered signals relative to a signal from a level of conventionally clear water are obtained. New features in the behavior of vertical profiles of an acoustic echo-signal are revealed due to the calibration. The results of this work will be used in subsequent analysis of the vertical and time variations in suspended sediment content in the near-shelf zone of the Black Sea.

Mesoscale variations in acoustic signals induced by atmospheric gravity waves.

PubMed

Chunchuzov, Igor; Kulichkov, Sergey; Perepelkin, Vitaly; Ziemann, Astrid; Arnold, Klaus; Kniffka, Anke

2009-02-01

The results of acoustic tomographic monitoring of the coherent structures in the lower atmosphere and the effects of these structures on acoustic signal parameters are analyzed in the present study. From the measurements of acoustic travel time fluctuations (periods 1 min-1 h) with distant receivers, the temporal fluctuations of the effective sound speed and wind speed are retrieved along different ray paths connecting an acoustic pulse source and several receivers. By using a coherence analysis of the fluctuations near spatially distanced ray turning points, the internal wave-associated fluctuations are filtered and their spatial characteristics (coherences, horizontal phase velocities, and spatial scales) are estimated. The capability of acoustic tomography in estimating wind shear near ground is shown. A possible mechanism describing the temporal modulation of the near-ground wind field by ducted internal waves in the troposphere is proposed.

Subsurface Event Detection and Classification Using Wireless Signal Networks

PubMed Central

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

2012-01-01

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

Lake bed classification using acoustic data

USGS Publications Warehouse

Yin, Karen K.; Li, Xing; Bonde, John; Richards, Carl; Cholwek, Gary

1998-01-01

As part of our effort to identify the lake bed surficial substrates using remote sensing data, this work designs pattern classifiers by multivariate statistical methods. Probability distribution of the preprocessed acoustic signal is analyzed first. A confidence region approach is then adopted to improve the design of the existing classifier. A technique for further isolation is proposed which minimizes the expected loss from misclassification. The devices constructed are applicable for real-time lake bed categorization. A mimimax approach is suggested to treat more general cases where the a priori probability distribution of the substrate types is unknown. Comparison of the suggested methods with the traditional likelihood ratio tests is discussed.

Impacts of underwater turbulence on acoustical and optical signals and their linkage.

PubMed

Hou, Weilin; Jarosz, Ewa; Woods, Sarah; Goode, Wesley; Weidemann, Alan

2013-02-25

Acoustical and optical signal transmission underwater is of vital interest for both civilian and military applications. The range and signal to noise during the transmission, as a function of system and water optical properties, in terms of absorption and scattering, determines the effectiveness of deployed electro-optical (EO) technology. The impacts from turbulence have been demonstrated to affect system performance comparable to those from particles by recent studies. This paper examines the impacts from underwater turbulence on both acoustic scattering and EO imaging degradation, and establishes a framework that can be used to correlate these. It is hypothesized here that underwater turbulence would influence the acoustic scattering cross section and the optical turbulence intensity coefficient in a similar manner. Data from a recent field campaign, Skaneateles Optical Turbulence Exercise (SOTEX, July, 2010) is used to examine the above relationship. Results presented here show strong correlation between the acoustic scattering cross-sections and the intensity coefficient related to the modulation transfer function of an EO imaging system. This significant finding will pave ways to utilize long range acoustical returns to predict EO system performance.

[Research on Time-frequency Characteristics of Magneto-acoustic Signal of Different Thickness Medium Based on Wave Summing Method].

PubMed

Zhang, Shunqi; Yin, Tao; Ma, Ren; Liu, Zhipeng

2015-08-01

Functional imaging method of biological electrical characteristics based on magneto-acoustic effect gives valuable information of tissue in early tumor diagnosis, therein time and frequency characteristics analysis of magneto-acoustic signal is important in image reconstruction. This paper proposes wave summing method based on Green function solution for acoustic source of magneto-acoustic effect. Simulations and analysis under quasi 1D transmission condition are carried out to time and frequency characteristics of magneto-acoustic signal of models with different thickness. Simulation results of magneto-acoustic signal were verified through experiments. Results of the simulation with different thickness showed that time-frequency characteristics of magneto-acoustic signal reflected thickness of sample. Thin sample, which is less than one wavelength of pulse, and thick sample, which is larger than one wavelength, showed different summed waveform and frequency characteristics, due to difference of summing thickness. Experimental results verified theoretical analysis and simulation results. This research has laid a foundation for acoustic source and conductivity reconstruction to the medium with different thickness in magneto-acoustic imaging.

Usage Autocorrelation Function in the Capacity of Indicator Shape of the Signal in Acoustic Emission Testing of Intricate Castings

NASA Astrophysics Data System (ADS)

Popkov, Artem

2016-01-01

The article contains information about acoustic emission signals analysing using autocorrelation function. Operation factors were analysed, such as shape of signal, the origins time and carrier frequency. The purpose of work is estimating the validity of correlations methods analysing signals. Acoustic emission signal consist of different types of waves, which propagate on different trajectories in object of control. Acoustic emission signal is amplitude-, phase- and frequency-modeling signal. It was described by carrier frequency at a given point of time. Period of signal make up 12.5 microseconds and carrier frequency make up 80 kHz for analysing signal. Usage autocorrelation function like indicator the origin time of acoustic emission signal raises validity localization of emitters.

Fatigue level estimation of monetary bills based on frequency band acoustic signals with feature selection by supervised SOM

NASA Astrophysics Data System (ADS)

Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued monetary bills adversely affect the daily operation of automated teller machines (ATMs). In order to make the classification of fatigued bills more efficient, the development of an automatic fatigued monetary bill classification method is desirable. We propose a new method by which to estimate the fatigue level of monetary bills from the feature-selected frequency band acoustic energy pattern of banking machines. By using a supervised self-organizing map (SOM), we effectively estimate the fatigue level using only the feature-selected frequency band acoustic energy pattern. Furthermore, the feature-selected frequency band acoustic energy pattern improves the estimation accuracy of the fatigue level of monetary bills by adding frequency domain information to the acoustic energy pattern. The experimental results with real monetary bill samples reveal the effectiveness of the proposed method.

Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach

Treesearch

F. Briggs; B. Lakshminarayanan; L. Neal; X.Z. Fern; R. Raich; S.F. Hadley; A.S. Hadley; M.G. Betts

2012-01-01

Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning...

The Relationship Between Acoustic Signal Typing and Perceptual Evaluation of Tracheoesophageal Voice Quality for Sustained Vowels.

PubMed

Clapham, Renee P; van As-Brooks, Corina J; van Son, Rob J J H; Hilgers, Frans J M; van den Brekel, Michiel W M

2015-07-01

To investigate the relationship between acoustic signal typing and perceptual evaluation of sustained vowels produced by tracheoesophageal (TE) speakers and the use of signal typing in the clinical setting. Two evaluators independently categorized 1.75-second segments of narrow-band spectrograms according to acoustic signal typing and independently evaluated the recording of the same segments on a visual analog scale according to overall perceptual acoustic voice quality. The relationship between acoustic signal typing and overall voice quality (as a continuous scale and as a four-point ordinal scale) was investigated and the proportion of inter-rater agreement as well as the reliability between the two measures is reported. The agreement between signal type (I-IV) and ordinal voice quality (four-point scale) was low but significant, and there was a significant linear relationship between the variables. Signal type correctly predicted less than half of the voice quality data. There was a significant main effect of signal type on continuous voice quality scores with significant differences in median quality scores between signal types I-IV, I-III, and I-II. Signal typing can be used as an adjunct to perceptual and acoustic evaluation of the same stimuli for TE speech as part of a multidimensional evaluation protocol. Signal typing in its current form provides limited predictive information on voice quality, and there is significant overlap between signal types II and III and perceptual categories. Future work should consider whether the current four signal types could be refined. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

TH-CD-201-06: Experimental Characterization of Acoustic Signals Generated in Water Following Clinical Photon and Electron Beam Irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hickling, S; El Naqa, I

Purpose: Previous work has demonstrated the detectability of acoustic waves induced following the irradiation of high density metals with radiotherapy linac photon beams. This work demonstrates the ability to experimentally detect such acoustic signals following both photon and electron irradiation in a more radiotherapy relevant material. The relationship between induced acoustic signal properties in water and the deposited dose distribution is explored, and the feasibility of exploiting such signals for radiotherapy dosimetry is demonstrated. Methods: Acoustic waves were experimentally induced in a water tank via the thermoacoustic effect following a single pulse of photon or electron irradiation produced by amore » clinical linac. An immersion ultrasound transducer was used to detect these acoustic waves in water and signals were read out on an oscilloscope. Results: Peaks and troughs in the detected acoustic signals were found to correspond to the location of gradients in the deposited dose distribution following both photon and electron irradiation. Signal amplitude was linearly related to the dose per pulse deposited by photon or electron beams at the depth of detection. Flattening filter free beams induced large acoustic signals, and signal amplitude decreased with depth after the depth of maximum dose. Varying the field size resulted in a temporal shift of the acoustic signal peaks and a change in the detected signal frequency. Conclusion: Acoustic waves can be detected in a water tank following irradiation by linac photon and electron beams with basic electronics, and have characteristics related to the deposited dose distribution. The physical location of dose gradients and the amount of dose deposited can be inferred from the location and magnitude of acoustic signal peaks. Thus, the detection of induced acoustic waves could be applied to photon and electron water tank and in vivo dosimetry. This work was supported in part by CIHR grants MOP-114910 and

Seismic and acoustic signal identification algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

LADD,MARK D.; ALAM,M. KATHLEEN; SLEEFE,GERARD E.

2000-04-03

This paper will describe an algorithm for detecting and classifying seismic and acoustic signals for unattended ground sensors. The algorithm must be computationally efficient and continuously process a data stream in order to establish whether or not a desired signal has changed state (turned-on or off). The paper will focus on describing a Fourier based technique that compares the running power spectral density estimate of the data to a predetermined signature in order to determine if the desired signal has changed state. How to establish the signature and the detection thresholds will be discussed as well as the theoretical statisticsmore » of the algorithm for the Gaussian noise case with results from simulated data. Actual seismic data results will also be discussed along with techniques used to reduce false alarms due to the inherent nonstationary noise environments found with actual data.« less

Dependencies and Ill-designed Parameters Within High-speed Videoendoscopy and Acoustic Signal Analysis.

PubMed

Schlegel, Patrick; Stingl, Michael; Kunduk, Melda; Kniesburges, Stefan; Bohr, Christopher; Döllinger, Michael

2018-05-31

The phonatory process is often judged during sustained phonation by analyzing the acoustic voice signal and the vocal fold vibrations. Many formulas and parameters have been suggested for qualifying the characteristics of the acoustic signal and the vocal fold vibrations during sustained phonation. These parameters are directly computed from the acoustic signal and the endoscopic glottal area waveform (GAW). The GAW is calculated from laryngeal high-speed videoendoscopy (HSV) recordings and describes the increase and decrease of the glottal area during the phonation process, that is, the opening and closing of the two oscillating vocal folds over time. However, some of the parameters have strong mathematical dependencies with one another and some are ill-defined. The purpose of this study is to identify mathematical dependencies between parameters with the aim of reducing their numbers and suggesting which parameters may best describe the properties of the GAW and the acoustical signal. In this preliminary investigation, 20 frequently used parameters are examined: 10 GAW only and 10 both GAW and acoustic parameters. In total 13 parameters can be neglected because of mathematical dependencies. In addition, nine of these parameters show problematic features that range from unexpected behavior to ill definition. Reducing the number of parameters appears to be necessary to standardize vocal fold function analysis. This may lead to better comparability of research results from different studies. Copyright © 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Substrate vibrations during acoustic signalling in the cicada Okanagana rimosa

PubMed Central

Stölting, Heiko; Moore, Thomas E.; Lakes-Harlan, Reinhard

2002-01-01

Males of the North American cicada Okanagana rimosa (Homoptera: Cicadidae, Tibicininae) emit loud airborne acoustic signals for intraspecific communication. Specialised vibratory signals could not be detected; however, the airborne signal induced substrate vibrations. Both auditory and vibratory spectra peak in the range from 7–10 kHz. Thus, the vibrations show similar frequency components to the sound spectrum within biologically relevant distances. These vibratory signals could be important as signals involved in mate localization and perhaps even as the context for the evolution of the ear in a group of parasitoid flies. PMID:15455036

The S-Matrix and Acoustic Signal Structure in Simple and Compound Waveguides.

DTIC Science & Technology

1982-12-01

RD-A125 583 THE S-MATRIX AND ACOUSTIC SIGNAL STRUCTURE IN SIMPLE- L/1 AND COMPOUND WAVEGUIDES(U) UTAH UNIV SALT LAKE CITY DEPT OF MATHEMATICS C H...WILCOX DEC 82 TSR-45 UNCLASSIFIED N6@8i4-76-C-8276 F/G 12/1 NL IEINEIIIIIIEIhllhlllllllIflllllflflflflflEN L-- U5-12 III,2,0 III.J --IL.,5 MICROCOP ...RESLUIO TETCHRNATIONA BUREA OF 20NADS16 THE S-MATRIX AND ACOUSTIC SIGNAL STRUCTURE IN SIMPLE AND COMPOUND WAVEGUIDES C. H. Wilcox Technical Simmary Report

Extraction of fault component from abnormal sound in diesel engines using acoustic signals

NASA Astrophysics Data System (ADS)

Dayong, Ning; Changle, Sun; Yongjun, Gong; Zengmeng, Zhang; Jiaoyi, Hou

2016-06-01

In this paper a method for extracting fault components from abnormal acoustic signals and automatically diagnosing diesel engine faults is presented. The method named dislocation superimposed method (DSM) is based on the improved random decrement technique (IRDT), differential function (DF) and correlation analysis (CA). The aim of DSM is to linearly superpose multiple segments of abnormal acoustic signals because of the waveform similarity of faulty components. The method uses sample points at the beginning of time when abnormal sound appears as the starting position for each segment. In this study, the abnormal sound belonged to shocking faulty type; thus, the starting position searching method based on gradient variance was adopted. The coefficient of similar degree between two same sized signals is presented. By comparing with a similar degree, the extracted fault component could be judged automatically. The results show that this method is capable of accurately extracting the fault component from abnormal acoustic signals induced by faulty shocking type and the extracted component can be used to identify the fault type.

Unsupervised classification of operator workload from brain signals.

PubMed

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects' error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Unsupervised classification of operator workload from brain signals

NASA Astrophysics Data System (ADS)

Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

2016-06-01

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

Acoustic signal emission monitoring as a novel method to predict steam pops during radiofrequency ablation: preliminary observations.

PubMed

Chik, William W B; Kosobrodov, Roman; Bhaskaran, Abhishek; Barry, Michael Anthony Tony; Nguyen, Doan Trang; Pouliopoulos, Jim; Byth, Karen; Sivagangabalan, Gopal; Thomas, Stuart P; Ross, David L; McEwan, Alistair; Kovoor, Pramesh; Thiagalingam, Aravinda

2015-04-01

Steam pop is an explosive rupture of cardiac tissue caused by tissue overheating above 100 °C, resulting in steam formation, predisposing to serious complications associated with radiofrequency (RF) ablations. However, there are currently no reliable techniques to predict the occurrence of steam pops. We propose the utility of acoustic signals emitted during RF ablation as a novel method to predict steam pop formation and potentially prevent serious complications. Radiofrequency generator parameters (power, impedance, and temperature) were temporally recorded during ablations performed in an in vitro bovine myocardial model. The acoustic system consisted of HTI-96-min hydrophone, microphone preamplifier, and sound card connected to a laptop computer. The hydrophone has the frequency range of 2 Hz to 30 kHz and nominal sensitivity in the range -240 to -165 dB. The sound was sampled at 96 kHz with 24-bit resolution. Output signal from the hydrophone was fed into the camera audio input to synchronize the video stream. An automated system was developed for the detection and analysis of acoustic events. Nine steam pops were observed. Three distinct sounds were identified as warning signals, each indicating rapid steam formation and its release from tissue. These sounds had a broad frequency range up to 6 kHz with several spectral peaks around 2-3 kHz. Subjectively, these warning signals were perceived as separate loud clicks, a quick succession of clicks, or continuous squeaking noise. Characteristic acoustic signals were identified preceding 80% of pops occurrence. Six cardiologists were able to identify 65% of acoustic signals accurately preceding the pop. An automated system identified the characteristic warning signals in 85% of cases. The mean time from the first acoustic signal to pop occurrence was 46 ± 20 seconds. The automated system had 72.7% sensitivity and 88.9% specificity for predicting pops. Easily identifiable characteristic acoustic emissions

Sources and Radiation Patterns of Volcano-Acoustic Signals Investigated with Field-Scale Chemical Explosions

NASA Astrophysics Data System (ADS)

Bowman, D. C.; Lees, J. M.; Taddeucci, J.; Graettinger, A. H.; Sonder, I.; Valentine, G.

2014-12-01

We investigate the processes that give rise to complex acoustic signals during volcanic blasts by monitoring buried chemical explosions with infrasound and audio range microphones, strong motion sensors, and high speed imagery. Acoustic waveforms vary with scaled depth of burial (SDOB, units in meters per cube root of joules), ranging from high amplitude, impulsive, gas expansion dominated signals at low SDOB to low amplitude, longer duration, ground motion dominated signals at high SDOB. Typically, the sudden upward acceleration of the substrate above the blast produces the first acoustic arrival, followed by a second pulse due to the eruption of pressurized gas at the surface. Occasionally, a third overpressure occurs when displaced material decelerates upon impact with the ground. The transition between ground motion dominated and gas release dominated acoustics ranges between 0.0038-0.0018 SDOB, respectively. For example, one explosion registering an SDOB=0.0031 produced two overpressure pulses of approximately equal amplitude, one due to ground motion, the other to gas release. Recorded volcano infrasound has also identified distinct ground motion and gas release components during explosions at Sakurajima, Santiaguito, and Karymsky volcanoes. Our results indicate that infrasound records may provide a proxy for the depth and energy of these explosions. Furthermore, while magma fragmentation models indicate the possibility of several explosions during a single vulcanian eruption (Alidibirov, Bull Volc., 1994), our results suggest that a single explosion can also produce complex acoustic signals. Thus acoustic records alone cannot be used to distinguish between single explosions and multiple closely-spaced blasts at volcanoes. Results from a series of lateral blasts during the 2014 field experiment further indicates whether vent geometry can produce directional acoustic radiation patterns like those observed at Tungarahua volcano (Kim et al., GJI, 2012). Beside

Signal processing methodologies for an acoustic fetal heart rate monitor

NASA Technical Reports Server (NTRS)

Pretlow, Robert A., III; Stoughton, John W.

1992-01-01

Research and development is presented of real time signal processing methodologies for the detection of fetal heart tones within a noise-contaminated signal from a passive acoustic sensor. A linear predictor algorithm is utilized for detection of the heart tone event and additional processing derives heart rate. The linear predictor is adaptively 'trained' in a least mean square error sense on generic fetal heart tones recorded from patients. A real time monitor system is described which outputs to a strip chart recorder for plotting the time history of the fetal heart rate. The system is validated in the context of the fetal nonstress test. Comparisons are made with ultrasonic nonstress tests on a series of patients. Comparative data provides favorable indications of the feasibility of the acoustic monitor for clinical use.

Acoustic discrimination of Southern Ocean zooplankton

NASA Astrophysics Data System (ADS)

Brierley, Andrew S.; Ward, Peter; Watkins, Jonathan L.; Goss, Catherine

Acoustic surveys in the vicinity of the sub-Antarctic island of South Georgia during a period of exceptionally calm weather revealed the existence of a number of horizontally extensive yet vertically discrete scattering layers in the upper 250 m of the water column. These layers were fished with a Longhurst-Hardy plankton recorder (LHPR) and a multiple-opening 8 m 2 rectangular mid-water trawl (RMT8). Analysis of catches suggested that each scattering layer was composed predominantly of a single species (biovolume>95%) of either the euphausiids Euphausia frigida or Thysanöessa macrura, the hyperiid amphipod Themisto gaudichaudii, or the eucalaniid copepod Rhincalanus gigas. Instrumentation on the nets allowed their trajectories to be reconstructed precisely, and thus catch data to be related directly to the corresponding acoustic signals. Discriminant function analysis of differences between mean volume backscattering strength at 38, 120 and 200 kHz separated echoes originating from each of the dominant scattering layers, and other signals identified as originating from Antarctic krill ( Euphausia superba), with an overall correct classification rate of 77%. Using echo intensity data alone, gathered using hardware commonly employed for fishery acoustics, it is therefore possible to discriminate in situ between several zooplanktonic taxa, taxa which in some instances exhibit similar gross morphological characteristics and have overlapping length- frequency distributions. Acoustic signals from the mysid Antarctomysis maxima could also be discriminated once information on target distribution was considered, highlighting the value of incorporating multiple descriptors of echo characteristics into signal identification procedures. The ability to discriminate acoustically between zooplankton taxa could be applied to provide improved acoustic estimates of species abundance, and to enhance field studies of zooplankton ecology, distribution and species interactions.

Data quality enhancement and knowledge discovery from relevant signals in acoustic emission

NASA Astrophysics Data System (ADS)

Mejia, Felipe; Shyu, Mei-Ling; Nanni, Antonio

2015-10-01

The increasing popularity of structural health monitoring has brought with it a growing need for automated data management and data analysis tools. Of great importance are filters that can systematically detect unwanted signals in acoustic emission datasets. This study presents a semi-supervised data mining scheme that detects data belonging to unfamiliar distributions. This type of outlier detection scheme is useful detecting the presence of new acoustic emission sources, given a training dataset of unwanted signals. In addition to classifying new observations (herein referred to as "outliers") within a dataset, the scheme generates a decision tree that classifies sub-clusters within the outlier context set. The obtained tree can be interpreted as a series of characterization rules for newly-observed data, and they can potentially describe the basic structure of different modes within the outlier distribution. The data mining scheme is first validated on a synthetic dataset, and an attempt is made to confirm the algorithms' ability to discriminate outlier acoustic emission sources from a controlled pencil-lead-break experiment. Finally, the scheme is applied to data from two fatigue crack-growth steel specimens, where it is shown that extracted rules can adequately describe crack-growth related acoustic emission sources while filtering out background "noise." Results show promising performance in filter generation, thereby allowing analysts to extract, characterize, and focus only on meaningful signals.

Temperature and Pressure Dependence of Signal Amplitudes for Electrostriction Laser-Induced Thermal Acoustics

NASA Technical Reports Server (NTRS)

Herring, Gregory C.

2015-01-01

The relative signal strength of electrostriction-only (no thermal grating) laser-induced thermal acoustics (LITA) in gas-phase air is reported as a function of temperature T and pressure P. Measurements were made in the free stream of a variable Mach number supersonic wind tunnel, where T and P are varied simultaneously as Mach number is varied. Using optical heterodyning, the measured signal amplitude (related to the optical reflectivity of the acoustic grating) was averaged for each of 11 flow conditions and compared to the expected theoretical dependence of a pure-electrostriction LITA process, where the signal is proportional to the square root of [P*P /( T*T*T)].

Portable Multi Hydrophone Array for Field and Laboratory Measurements of Odontocete Acoustic Signals

DTIC Science & Technology

2014-09-30

false killer whale . Our analysis will also be conducted with current passive acoustic monitoring detectors and classifiers in order to assess if the...obtain horizontal and vertical beam patterns of acoustic signals of a false killer whale and a bottlenose dolphin. The data is currently being

Classification of EEG Signals Based on Pattern Recognition Approach.

PubMed

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Classification of EEG Signals Based on Pattern Recognition Approach

PubMed Central

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy. PMID

Lightning Location Using Acoustic Signals

NASA Astrophysics Data System (ADS)

Badillo, E.; Arechiga, R. O.; Thomas, R. J.

2013-05-01

In the summer of 2011 and 2012 a network of acoustic arrays was deployed in the Magdalena mountains of central New Mexico to locate lightning flashes. A Times-Correlation (TC) ray-tracing-based-technique was developed in order to obtain the location of lightning flashes near the network. The TC technique, locates acoustic sources from lightning. It was developed to complement the lightning location of RF sources detected by the Lightning Mapping Array (LMA) developed at Langmuir Laboratory, in New Mexico Tech. The network consisted of four arrays with four microphones each. The microphones on each array were placed in a triangular configuration with one of the microphones in the center of the array. The distance between the central microphone and the rest of them was about 30 m. The distance between centers of the arrays ranged from 500 m to 1500 m. The TC technique uses times of arrival (TOA) of acoustic waves to trace back the location of thunder sources. In order to obtain the times of arrival, the signals were filtered in a frequency band of 2 to 20 hertz and cross-correlated. Once the times of arrival were obtained, the Levenberg-Marquardt algorithm was applied to locate the spatial coordinates (x,y, and z) of thunder sources. Two techniques were used and contrasted to compute the accuracy of the TC method: Nearest-Neighbors (NN), between acoustic and LMA located sources, and standard deviation from the curvature matrix of the system as a measure of dispersion of the results. For the best case scenario, a triggered lightning event, the TC method applied with four microphones, located sources with a median error of 152 m and 142.9 m using nearest-neighbors and standard deviation respectively.; Results of the TC method in the lightning event recorded at 18:47:35 UTC, August 6, 2012. Black dots represent the results computed. Light color dots represent the LMA data for the same event. The results were obtained with the MGTM station (four channels). This figure

Infrasonic and seismic signals from earthquakes and explosions observed with Plostina seismo-acoustic array

NASA Astrophysics Data System (ADS)

Ghica, D.; Ionescu, C.

2012-04-01

Plostina seismo-acoustic array has been recently deployed by the National Institute for Earth Physics in the central part of Romania, near the Vrancea epicentral area. The array has a 2.5 km aperture and consists of 7 seismic sites (PLOR) and 7 collocated infrasound instruments (IPLOR). The array is being used to assess the importance of collocated seismic and acoustic sensors for the purposes of (1) seismic monitoring of the local and regional events, and (2) acoustic measurement, consisting of detection of the infrasound events (explosions, mine and quarry blasts, earthquakes, aircraft etc.). This paper focuses on characterization of infrasonic and seismic signals from the earthquakes and explosions (accidental and mining type). Two Vrancea earthquakes with magnitude above 5.0 were selected to this study: one occurred on 1st of May 2011 (MD = 5.3, h = 146 km), and the other one, on 4th October 2011 (MD = 5.2, h = 142 km). The infrasonic signals from the earthquakes have the appearance of the vertical component of seismic signals. Because the mechanism of the infrasonic wave formation is the coupling of seismic waves with the atmosphere, trace velocity values for such signals are compatible with the characteristics of the various seismic phases observed with PLOR array. The study evaluates and characterizes, as well, infrasound and seismic data recorded from the explosion caused by the military accident produced at Evangelos Florakis Naval Base, in Cyprus, on 11th July 2011. Additionally, seismo-acoustic signals presumed to be related to strong mine and quarry blasts were investigated. Ground truth of mine observations provides validation of this interpretation. The combined seismo-acoustic analysis uses two types of detectors for signal identification: one is the automatic detector DFX-PMCC, applied for infrasound detection and characterization, while the other one, which is used for seismic data, is based on array processing techniques (beamforming and frequency

Shared developmental and evolutionary origins for neural basis of vocal–acoustic and pectoral–gestural signaling

PubMed Central

Bass, Andrew H.; Chagnaud, Boris P.

2012-01-01

Acoustic signaling behaviors are widespread among bony vertebrates, which include the majority of living fishes and tetrapods. Developmental studies in sound-producing fishes and tetrapods indicate that central pattern generating networks dedicated to vocalization originate from the same caudal hindbrain rhombomere (rh) 8-spinal compartment. Together, the evidence suggests that vocalization and its morphophysiological basis, including mechanisms of vocal–respiratory coupling that are widespread among tetrapods, are ancestral characters for bony vertebrates. Premotor-motor circuitry for pectoral appendages that function in locomotion and acoustic signaling develops in the same rh8-spinal compartment. Hence, vocal and pectoral phenotypes in fishes share both developmental origins and roles in acoustic communication. These findings lead to the proposal that the coupling of more highly derived vocal and pectoral mechanisms among tetrapods, including those adapted for nonvocal acoustic and gestural signaling, originated in fishes. Comparative studies further show that rh8 premotor populations have distinct neurophysiological properties coding for equally distinct behavioral attributes such as call duration. We conclude that neural network innovations in the spatiotemporal patterning of vocal and pectoral mechanisms of social communication, including forelimb gestural signaling, have their evolutionary origins in the caudal hindbrain of fishes. PMID:22723366

Automated Classification of Power Signals

DTIC Science & Technology

2008-06-01

determine when a transient occurs. The identification of this signal can then be determined by an expert classifier and a series of these...the manual identification and classification of system events. Once events were located, the characteristics were examined to determine if system... identification code, which varies depending on the system classifier that is specified. Figure 3-7 provides an example of a Linux directory containing

Investigation of signal models and methods for evaluating structures of processing telecommunication information exchange systems under acoustic noise conditions

NASA Astrophysics Data System (ADS)

Kropotov, Y. A.; Belov, A. A.; Proskuryakov, A. Y.; Kolpakov, A. A.

2018-05-01

The paper considers models and methods for estimating signals during the transmission of information messages in telecommunication systems of audio exchange. One-dimensional probability distribution functions that can be used to isolate useful signals, and acoustic noise interference are presented. An approach to the estimation of the correlation and spectral functions of the parameters of acoustic signals is proposed, based on the parametric representation of acoustic signals and the components of the noise components. The paper suggests an approach to improving the efficiency of interference cancellation and highlighting the necessary information when processing signals from telecommunications systems. In this case, the suppression of acoustic noise is based on the methods of adaptive filtering and adaptive compensation. The work also describes the models of echo signals and the structure of subscriber devices in operational command telecommunications systems.

A study on locating the sonic source of sinusoidal magneto-acoustic signals using a vector method.

PubMed

Zhang, Shunqi; Zhou, Xiaoqing; Ma, Ren; Yin, Tao; Liu, Zhipeng

2015-01-01

Methods based on the magnetic-acoustic effect are of great significance in studying the electrical imaging properties of biological tissues and currents. The continuous wave method, which is commonly used, can only detect the current amplitude without the sound source position. Although the pulse mode adopted in magneto-acoustic imaging can locate the sonic source, the low measuring accuracy and low SNR has limited its application. In this study, a vector method was used to solve and analyze the magnetic-acoustic signal based on the continuous sine wave mode. This study includes theory modeling of the vector method, simulations to the line model, and experiments with wire samples to analyze magneto-acoustic (MA) signal characteristics. The results showed that the amplitude and phase of the MA signal contained the location information of the sonic source. The amplitude and phase obeyed the vector theory in the complex plane. This study sets a foundation for a new technique to locate sonic sources for biomedical imaging of tissue conductivity. It also aids in studying biological current detecting and reconstruction based on the magneto-acoustic effect.

Underwater target classification using wavelet packets and neural networks.

PubMed

Azimi-Sadjadi, M R; Yao, D; Huang, Q; Dobeck, G J

2000-01-01

In this paper, a new subband-based classification scheme is developed for classifying underwater mines and mine-like targets from the acoustic backscattered signals. The system consists of a feature extractor using wavelet packets in conjunction with linear predictive coding (LPC), a feature selection scheme, and a backpropagation neural-network classifier. The data set used for this study consists of the backscattered signals from six different objects: two mine-like targets and four nontargets for several aspect angles. Simulation results on ten different noisy realizations and for signal-to-noise ratio (SNR) of 12 dB are presented. The receiver operating characteristic (ROC) curve of the classifier generated based on these results demonstrated excellent classification performance of the system. The generalization ability of the trained network was demonstrated by computing the error and classification rate statistics on a large data set. A multiaspect fusion scheme was also adopted in order to further improve the classification performance.

System and method for characterizing synthesizing and/or canceling out acoustic signals from inanimate sound sources

DOEpatents

Holzrichter, John F.; Burnett, Greg C.; Ng, Lawrence C.

2003-01-01

A system and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources is disclosed. Propagating wave electromagnetic sensors monitor excitation sources in sound producing systems, such as machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The methods disclosed enable accurate calculation of matched transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

System and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources

DOEpatents

Holzrichter, John F; Burnett, Greg C; Ng, Lawrence C

2013-05-21

A system and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources is disclosed. Propagating wave electromagnetic sensors monitor excitation sources in sound producing systems, such as machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The methods disclosed enable accurate calculation of matched transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

System and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources

DOEpatents

Holzrichter, John F.; Burnett, Greg C.; Ng, Lawrence C.

2007-10-16

A system and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources is disclosed. Propagating wave electromagnetic sensors monitor excitation sources in sound producing systems, such as machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The methods disclosed enable accurate calculation of matched transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

Analysis and Classification of Voice Pathologies Using Glottal Signal Parameters.

PubMed

Forero M, Leonardo A; Kohler, Manoela; Vellasco, Marley M B R; Cataldo, Edson

2016-09-01

The classification of voice diseases has many applications in health, in diseases treatment, and in the design of new medical equipment for helping doctors in diagnosing pathologies related to the voice. This work uses the parameters of the glottal signal to help the identification of two types of voice disorders related to the pathologies of the vocal folds: nodule and unilateral paralysis. The parameters of the glottal signal are obtained through a known inverse filtering method, and they are used as inputs to an Artificial Neural Network, a Support Vector Machine, and also to a Hidden Markov Model, to obtain the classification, and to compare the results, of the voice signals into three different groups: speakers with nodule in the vocal folds; speakers with unilateral paralysis of the vocal folds; and speakers with normal voices, that is, without nodule or unilateral paralysis present in the vocal folds. The database is composed of 248 voice recordings (signals of vowels production) containing samples corresponding to the three groups mentioned. In this study, a larger database was used for the classification when compared with similar studies, and its classification rate is superior to other studies, reaching 97.2%. Copyright © 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Beeping and piping: characterization of two mechano-acoustic signals used by honey bees in swarming

NASA Astrophysics Data System (ADS)

Schlegel, Thomas; Visscher, P. Kirk; Seeley, Thomas D.

2012-12-01

Of the many signals used by honey bees during the process of swarming, two of them—the stop signal and the worker piping signal—are not easily distinguished for both are mechano-acoustic signals produced by scout bees who press their bodies against other bees while vibrating their wing muscles. To clarify the acoustic differences between these two signals, we recorded both signals from the same swarm and at the same time, and compared them in terms of signal duration, fundamental frequency, and frequency modulation. Stop signals and worker piping signals differ in all three variables: duration, 174 ± 64 vs. 602 ± 377 ms; fundamental frequency, 407 vs. 451 Hz; and frequency modulation, absent vs. present. While it remains unclear which differences the bees use to distinguish the two signals, it is clear that they do so for the signals have opposite effects. Stop signals cause inhibition of actively dancing scout bees whereas piping signals cause excitation of quietly resting non-scout bees.

Age group estimation in free-ranging African elephants based on acoustic cues of low-frequency rumbles

PubMed Central

Stoeger, Angela S.; Zeppelzauer, Matthias; Baotic, Anton

2015-01-01

Animal vocal signals are increasingly used to monitor wildlife populations and to obtain estimates of species occurrence and abundance. In the future, acoustic monitoring should function not only to detect animals, but also to extract detailed information about populations by discriminating sexes, age groups, social or kin groups, and potentially individuals. Here we show that it is possible to estimate age groups of African elephants (Loxodonta africana) based on acoustic parameters extracted from rumbles recorded under field conditions in a National Park in South Africa. Statistical models reached up to 70 % correct classification to four age groups (infants, calves, juveniles, adults) and 95 % correct classification when categorising into two groups (infants/calves lumped into one group versus adults). The models revealed that parameters representing absolute frequency values have the most discriminative power. Comparable classification results were obtained by fully automated classification of rumbles by high-dimensional features that represent the entire spectral envelope, such as MFCC (75 % correct classification) and GFCC (74 % correct classification). The reported results and methods provide the scientific foundation for a future system that could potentially automatically estimate the demography of an acoustically monitored elephant group or population. PMID:25821348

Development of an Acoustic Signal Analysis Tool “Auto-F” Based on the Temperament Scale

NASA Astrophysics Data System (ADS)

Modegi, Toshio

The MIDI interface is originally designed for electronic musical instruments but we consider this music-note based coding concept can be extended for general acoustic signal description. We proposed applying the MIDI technology to coding of bio-medical auscultation sound signals such as heart sounds for retrieving medical records and performing telemedicine. Then we have tried to extend our encoding targets including vocal sounds, natural sounds and electronic bio-signals such as ECG, using Generalized Harmonic Analysis method. Currently, we are trying to separate vocal sounds included in popular songs and encode both vocal sounds and background instrumental sounds into separate MIDI channels. And also, we are trying to extract articulation parameters such as MIDI pitch-bend parameters in order to reproduce natural acoustic sounds using a GM-standard MIDI tone generator. In this paper, we present an overall algorithm of our developed acoustic signal analysis tool, based on those research works, which can analyze given time-based signals on the musical temperament scale. The prominent feature of this tool is producing high-precision MIDI codes, which reproduce the similar signals as the given source signal using a GM-standard MIDI tone generator, and also providing analyzed texts in the XML format.

Near- Source, Seismo-Acoustic Signals Accompanying a NASCAR Race at the Texas Motor Speedway

NASA Astrophysics Data System (ADS)

Stump, B. W.; Hayward, C.; Underwood, R.; Howard, J. E.; MacPhail, M. D.; Golden, P.; Endress, A.

2014-12-01

Near-source, seismo-acoustic observations provide a unique opportunity to characterize urban sources, remotely sense human activities including vehicular traffic and monitor large engineering structures. Energy separately coupled into the solid earth and atmosphere provides constraints on not only the location of these sources but also the physics of the generating process. Conditions and distances at which these observations can be made are dependent upon not only local geological conditions but also atmospheric conditions at the time of the observations. In order to address this range of topics, an empirical, seismo-acoustic study was undertaken in and around the Texas Motor Speedway in the Dallas-Ft. Worth area during the first week of April 2014 at which time a range of activities associated with a series of NASCAR races occurred. Nine, seismic sensors were deployed around the 1.5-mile track for purposes of documenting the direct-coupled seismic energy from the passage of the cars and other vehicles on the track. Six infrasound sensors were deployed on a rooftop in a rectangular array configuration designed to provide high frequency beam forming for acoustic signals. Finally, a five-element infrasound array was deployed outside the track in order to characterize how the signals propagate away from the sources in the near-source region. Signals recovered from within the track were able to track and characterize the motion of a variety of vehicles during the race weekend including individual racecars. Seismic data sampled at 1000 sps documented strong Doppler effects as the cars approached and moved away from individual sensors. There were faint seismic signals that arrived at seismic velocity but local acoustic to seismic coupling as supported by the acoustic observations generated the majority of seismic signals. Actual seismic ground motions were small as demonstrated by the dominance of regional seismic signals from a magnitude 4.0 earthquake that arrived at

Classification of electroencephalograph signals using time-frequency decomposition and linear discriminant analysis

NASA Astrophysics Data System (ADS)

Szuflitowska, B.; Orlowski, P.

2017-08-01

Automated detection system consists of two key steps: extraction of features from EEG signals and classification for detection of pathology activity. The EEG sequences were analyzed using Short-Time Fourier Transform and the classification was performed using Linear Discriminant Analysis. The accuracy of the technique was tested on three sets of EEG signals: epilepsy, healthy and Alzheimer's Disease. The classification error below 10% has been considered a success. The higher accuracy are obtained for new data of unknown classes than testing data. The methodology can be helpful in differentiation epilepsy seizure and disturbances in the EEG signal in Alzheimer's Disease.

A framework for the damage evaluation of acoustic emission signals through Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Siracusano, Giulio; Lamonaca, Francesco; Tomasello, Riccardo; Garescì, Francesca; Corte, Aurelio La; Carnì, Domenico Luca; Carpentieri, Mario; Grimaldi, Domenico; Finocchio, Giovanni

2016-06-01

The acoustic emission (AE) is a powerful and potential nondestructive testing method for structural monitoring in civil engineering. Here, we show how systematic investigation of crack phenomena based on AE data can be significantly improved by the use of advanced signal processing techniques. Such data are a fundamental source of information that can be used as the basis for evaluating the status of the material, thereby paving the way for a new frontier of innovation made by data-enabled analytics. In this article, we propose a framework based on the Hilbert-Huang Transform for the evaluation of material damages that (i) facilitates the systematic employment of both established and promising analysis criteria, and (ii) provides unsupervised tools to achieve an accurate classification of the fracture type, the discrimination between longitudinal (P-) and traversal (S-) waves related to an AE event. The experimental validation shows promising results for a reliable assessment of the health status through the monitoring of civil infrastructures.

Use of acoustic classification of sidescan sonar data for mapping benthic habitat in the Northern Channel Islands, California

USGS Publications Warehouse

Cochrane, Guy R.; Lafferty, Kevin D.

2002-01-01

Highly reflective seafloor features imaged by sidescan sonar in nearshore waters off the Northern Channel Islands (California, USA) have been observed in subsequent submersible dives to be areas of thin sand covering bedrock. Adjacent areas of rocky seafloor, suitable as habitat for endangered species of abalone and rockfish, and encrusting organisms, cannot be differentiated from the areas of thin sand on the basis of acoustic backscatter (i.e. grey level) alone. We found second-order textural analysis of sidescan sonar data useful to differentiate the bottom types where data is not degraded by near-range distortion (caused by slant-range and ground-range corrections), and where data is not degraded by far-range signal attenuation. Hand editing based on submersible observations is necessary to completely convert the sidescan sonar image to a bottom character classification map suitable for habitat mapping.

Adaptive fault feature extraction from wayside acoustic signals from train bearings

NASA Astrophysics Data System (ADS)

Zhang, Dingcheng; Entezami, Mani; Stewart, Edward; Roberts, Clive; Yu, Dejie

2018-07-01

Wayside acoustic detection of train bearing faults plays a significant role in maintaining safety in the railway transport system. However, the bearing fault information is normally masked by strong background noises and harmonic interferences generated by other components (e.g. axles and gears). In order to extract the bearing fault feature information effectively, a novel method called improved singular value decomposition (ISVD) with resonance-based signal sparse decomposition (RSSD), namely the ISVD-RSSD method, is proposed in this paper. A Savitzky-Golay (S-G) smoothing filter is used to filter singular vectors (SVs) in the ISVD method as an extension of the singular value decomposition (SVD) theorem. Hilbert spectrum entropy and a stepwise optimisation strategy are used to optimize the S-G filter's parameters. The RSSD method is able to nonlinearly decompose the wayside acoustic signal of a faulty train bearing into high and low resonance components, the latter of which contains bearing fault information. However, the high level of noise usually results in poor decomposition results from the RSSD method. Hence, the collected wayside acoustic signal must first be de-noised using the ISVD component of the ISVD-RSSD method. Next, the de-noised signal is decomposed by using the RSSD method. The obtained low resonance component is then demodulated with a Hilbert transform such that the bearing fault can be detected by observing Hilbert envelope spectra. The effectiveness of the ISVD-RSSD method is verified through both laboratory field-based experiments as described in the paper. The results indicate that the proposed method is superior to conventional spectrum analysis and ensemble empirical mode decomposition methods.

Shallow-Water Mud Acoustics

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Shallow- Water Mud Acoustics William L. Siegmann...shallow water over mud sediments and of acoustic detection, localization, and classification of objects buried in mud. OBJECTIVES • Develop...including long-range conveyance of information; detection, localization, and classification of objects buried in mud; and improvement of shallow water

Acoustic-Seismic Coupling of Broadband Signals - Analysis of Potential Disturbances during CTBT On-Site Inspection Measurements

NASA Astrophysics Data System (ADS)

Liebsch, Mattes; Altmann, Jürgen

2015-04-01

For the verification of the Comprehensive Nuclear Test Ban Treaty (CTBT) the precise localisation of possible underground nuclear explosion sites is important. During an on-site inspection (OSI) sensitive seismic measurements of aftershocks can be performed, which, however, can be disturbed by other signals. To improve the quality and effectiveness of these measurements it is essential to understand those disturbances so that they can be reduced or prevented. In our work we focus on disturbing signals caused by airborne sources: When the sound of aircraft (as often used by the inspectors themselves) hits the ground, it propagates through pores in the soil. Its energy is transferred to the ground and soil vibrations are created which can mask weak aftershock signals. The understanding of the coupling of acoustic waves to the ground is still incomplete. However, it is necessary to improve the performance of an OSI, e.g. to address potential consequences for the sensor placement, the helicopter trajectories etc. We present our recent advances in this field. We performed several measurements to record sound pressure and soil velocity produced by various sources, e.g. broadband excitation by jet aircraft passing overhead and signals artificially produced by a speaker. For our experimental set-up microphones were placed close to the ground and geophones were buried in different depths in the soil. Several sensors were shielded from the directly incident acoustic signals by a box coated with acoustic damping material. While sound pressure under the box was strongly reduced, the soil velocity measured under the box was just slightly smaller than outside of it. Thus these soil vibrations were mostly created outside the box and travelled through the soil to the sensors. This information is used to estimate characteristic propagation lengths of the acoustically induced signals in the soil. In the seismic data we observed interference patterns which are likely caused by the

The acoustic adaptation hypothesis in a widely distributed South American frog: Southernmost signals propagate better.

PubMed

Velásquez, Nelson A; Moreno-Gómez, Felipe N; Brunetti, Enzo; Penna, Mario

2018-05-03

Animal communication occurs in environments that affect the properties of signals as they propagate from senders to receivers. We studied the geographic variation of the advertisement calls of male Pleurodema thaul individuals from eight localities in Chile. Furthermore, by means of signal propagation experiments, we tested the hypothesis that local calls are better transmitted and less degraded than foreign calls (i.e. acoustic adaptation hypothesis). Overall, the advertisement calls varied greatly along the distribution of P. thaul in Chile, and it was possible to discriminate localities grouped into northern, central and southern stocks. Propagation distance affected signal amplitude and spectral degradation in all localities, but temporal degradation was only affected by propagation distance in one out of seven localities. Call origin affected signal amplitude in five out of seven localities and affected spectral and temporal degradation in six out of seven localities. In addition, in northern localities, local calls degraded more than foreign calls, and in southern localities the opposite was observed. The lack of a strict optimal relationship between signal characteristics and environment indicates partial concordance with the acoustic adaptation hypothesis. Inter-population differences in selectivity for call patterns may compensate for such environmental constraints on acoustic communication.

A machine learning approach to multi-level ECG signal quality classification.

PubMed

Li, Qiao; Rajagopalan, Cadathur; Clifford, Gari D

2014-12-01

Current electrocardiogram (ECG) signal quality assessment studies have aimed to provide a two-level classification: clean or noisy. However, clinical usage demands more specific noise level classification for varying applications. This work outlines a five-level ECG signal quality classification algorithm. A total of 13 signal quality metrics were derived from segments of ECG waveforms, which were labeled by experts. A support vector machine (SVM) was trained to perform the classification and tested on a simulated dataset and was validated using data from the MIT-BIH arrhythmia database (MITDB). The simulated training and test datasets were created by selecting clean segments of the ECG in the 2011 PhysioNet/Computing in Cardiology Challenge database, and adding three types of real ECG noise at different signal-to-noise ratio (SNR) levels from the MIT-BIH Noise Stress Test Database (NSTDB). The MITDB was re-annotated for five levels of signal quality. Different combinations of the 13 metrics were trained and tested on the simulated datasets and the best combination that produced the highest classification accuracy was selected and validated on the MITDB. Performance was assessed using classification accuracy (Ac), and a single class overlap accuracy (OAc), which assumes that an individual type classified into an adjacent class is acceptable. An Ac of 80.26% and an OAc of 98.60% on the test set were obtained by selecting 10 metrics while 57.26% (Ac) and 94.23% (OAc) were the numbers for the unseen MITDB validation data without retraining. By performing the fivefold cross validation, an Ac of 88.07±0.32% and OAc of 99.34±0.07% were gained on the validation fold of MITDB. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Detection of Cracking Levels in Brittle Rocks by Parametric Analysis of the Acoustic Emission Signals

NASA Astrophysics Data System (ADS)

Moradian, Zabihallah; Einstein, Herbert H.; Ballivy, Gerard

2016-03-01

Determination of the cracking levels during the crack propagation is one of the key challenges in the field of fracture mechanics of rocks. Acoustic emission (AE) is a technique that has been used to detect cracks as they occur across the specimen. Parametric analysis of AE signals and correlating these parameters (e.g., hits and energy) to stress-strain plots of rocks let us detect cracking levels properly. The number of AE hits is related to the number of cracks, and the AE energy is related to magnitude of the cracking event. For a full understanding of the fracture process in brittle rocks, prismatic specimens of granite containing pre-existing flaws have been tested in uniaxial compression tests, and their cracking process was monitored with both AE and high-speed video imaging. In this paper, the characteristics of the AE parameters and the evolution of cracking sequences are analyzed for every cracking level. Based on micro- and macro-crack damage, a classification of cracking levels is introduced. This classification contains eight stages (1) crack closure, (2) linear elastic deformation, (3) micro-crack initiation (white patch initiation), (4) micro-crack growth (stable crack growth), (5) micro-crack coalescence (macro-crack initiation), (6) macro-crack growth (unstable crack growth), (7) macro-crack coalescence and (8) failure.

The effect of artificial rain on backscattered acoustic signal: first measurements

NASA Astrophysics Data System (ADS)

Titchenko, Yuriy; Karaev, Vladimir; Meshkov, Evgeny; Goldblat, Vladimir

The problem of rain influencing on a characteristics of backscattered ultrasonic and microwave signal by water surface is considered. The rain influence on backscattering process of electromagnetic waves was investigated in laboratory and field experiments, for example [1-3]. Raindrops have a significant impact on backscattering of microwave and influence on wave spectrum measurement accuracy by string wave gauge. This occurs due to presence of raindrops in atmosphere and modification of the water surface. For measurements of water surface characteristics during precipitation we propose to use an acoustic system. This allows us obtaining of the water surface parameters independently on precipitation in atmosphere. The measurements of significant wave height of water surface using underwater acoustical systems are well known [4, 5]. Moreover, the variance of orbital velocity can be measure using these systems. However, these methods cannot be used for measurements of slope variance and the other second statistical moments of water surface that required for analyzing the radar backscatter signal. An original design Doppler underwater acoustic wave gauge allows directly measuring the surface roughness characteristics that affect on electromagnetic waves backscattering of the same wavelength [6]. Acoustic wave gauge is Doppler ultrasonic sonar which is fixed near the bottom on the floating disk. Measurements are carried out at vertically orientation of sonar antennas towards water surface. The first experiments were conducted with the first model of an acoustic wave gauge. The acoustic wave gauge (8 mm wavelength) is equipped with a transceiving antenna with a wide symmetrical antenna pattern. The gauge allows us to measure Doppler spectrum and cross section of backscattered signal. Variance of orbital velocity vertical component can be retrieved from Doppler spectrum with high accuracy. The result of laboratory and field experiments during artificial rain is presented

Non-heuristic automatic techniques for overcoming low signal-to-noise-ratio bias of localization microscopy and multiple signal classification algorithm.

PubMed

Agarwal, Krishna; Macháň, Radek; Prasad, Dilip K

2018-03-21

Localization microscopy and multiple signal classification algorithm use temporal stack of image frames of sparse emissions from fluorophores to provide super-resolution images. Localization microscopy localizes emissions in each image independently and later collates the localizations in all the frames, giving same weight to each frame irrespective of its signal-to-noise ratio. This results in a bias towards frames with low signal-to-noise ratio and causes cluttered background in the super-resolved image. User-defined heuristic computational filters are employed to remove a set of localizations in an attempt to overcome this bias. Multiple signal classification performs eigen-decomposition of the entire stack, irrespective of the relative signal-to-noise ratios of the frames, and uses a threshold to classify eigenimages into signal and null subspaces. This results in under-representation of frames with low signal-to-noise ratio in the signal space and over-representation in the null space. Thus, multiple signal classification algorithms is biased against frames with low signal-to-noise ratio resulting into suppression of the corresponding fluorophores. This paper presents techniques to automatically debias localization microscopy and multiple signal classification algorithm of these biases without compromising their resolution and without employing heuristics, user-defined criteria. The effect of debiasing is demonstrated through five datasets of invitro and fixed cell samples.

Acousto-Optic Interaction in Surface Acoustic Waves and Its Application to Real Time Signal Processing.

DTIC Science & Technology

1977-12-30

ACOUSTO - OPTIC INTERACTION IN SURFACE ACOUSTIC WAVES AND ITS APP--ETC(U) DEC 77 0 SCHUMER, P DAS NOOOIJ -75-C-0772 NCLASSIFIED MA-ONR-30 Nt.EE E’h...CHART NAT*NAL BUREAU OF STANDARDS 1-63- ACOUSTO - OPTIC INTERACTION IN SURFACE ACOUSTIC WAVES AND ITS APPLICATION TO REAL TIME SIGNAL PROCESSING By 00 D... Acousto - optics , Integrated optics, Optical Signal Processing. 20. AbSKTRACT (Continue an reverse side it neceary and idewnt& by block mum ber) The

Acoustic Signal Processing for Pipe Condition Assessment (WaterRF Report 4360)

EPA Science Inventory

Unique to prestressed concrete cylinder pipe (PCCP), individual wire breaks create an excitation in the pipe wall that may vary in response to the remaining compression of the pipe core. This project was designed to improve acoustic signal processing for pipe condition assessment...

System and method for investigating sub-surface features of a rock formation with acoustic sources generating conical broadcast signals

DOEpatents

Vu, Cung Khac; Skelt, Christopher; Nihei, Kurt; Johnson, Paul A.; Guyer, Robert; Ten Cate, James A.; Le Bas, Pierre -Yves; Larmat, Carene S.

2015-08-18

A method of interrogating a formation includes generating a conical acoustic signal, at a first frequency--a second conical acoustic signal at a second frequency each in the between approximately 500 Hz and 500 kHz such that the signals intersect in a desired intersection volume outside the borehole. The method further includes receiving, a difference signal returning to the borehole resulting from a non-linear mixing of the signals in a mixing zone within the intersection volume.

Classification of spontaneous EEG signals in migraine

NASA Astrophysics Data System (ADS)

Bellotti, R.; De Carlo, F.; de Tommaso, M.; Lucente, M.

2007-08-01

We set up a classification system able to detect patients affected by migraine without aura, through the analysis of their spontaneous EEG patterns. First, the signals are characterized by means of wavelet-based features, than a supervised neural network is used to classify the multichannel data. For the feature extraction, scale-dependent and scale-independent methods are considered with a variety of wavelet functions. Both the approaches provide very high and almost comparable classification performances. A complete separation of the two groups is obtained when the data are plotted in the plane spanned by two suitable neural outputs.

Spatial acoustic signal processing for immersive communication

NASA Astrophysics Data System (ADS)

Atkins, Joshua

Computing is rapidly becoming ubiquitous as users expect devices that can augment and interact naturally with the world around them. In these systems it is necessary to have an acoustic front-end that is able to capture and reproduce natural human communication. Whether the end point is a speech recognizer or another human listener, the reduction of noise, reverberation, and acoustic echoes are all necessary and complex challenges. The focus of this dissertation is to provide a general method for approaching these problems using spherical microphone and loudspeaker arrays.. In this work, a theory of capturing and reproducing three-dimensional acoustic fields is introduced from a signal processing perspective. In particular, the decomposition of the spatial part of the acoustic field into an orthogonal basis of spherical harmonics provides not only a general framework for analysis, but also many processing advantages. The spatial sampling error limits the upper frequency range with which a sound field can be accurately captured or reproduced. In broadband arrays, the cost and complexity of using multiple transducers is an issue. This work provides a flexible optimization method for determining the location of array elements to minimize the spatial aliasing error. The low frequency array processing ability is also limited by the SNR, mismatch, and placement error of transducers. To address this, a robust processing method is introduced and used to design a reproduction system for rendering over arbitrary loudspeaker arrays or binaurally over headphones. In addition to the beamforming problem, the multichannel acoustic echo cancellation (MCAEC) issue is also addressed. A MCAEC must adaptively estimate and track the constantly changing loudspeaker-room-microphone response to remove the sound field presented over the loudspeakers from that captured by the microphones. In the multichannel case, the system is overdetermined and many adaptive schemes fail to converge to

Classification of subsurface objects using singular values derived from signal frames

DOEpatents

Chambers, David H; Paglieroni, David W

2014-05-06

The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

Classification of EEG signals using a genetic-based machine learning classifier.

PubMed

Skinner, B T; Nguyen, H T; Liu, D K

2007-01-01

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

Attenuation of the Acoustic Signal Propagating Through a Bubbly Liquid Layer

NASA Astrophysics Data System (ADS)

Gubaidullin, D. A.; Nikiforov, A. A.

2018-01-01

The acoustic signal dynamics in a five-layer medium containing two liquid layers with polydisperse gas bubbles has been investigated. Calculations have been made for the interaction between the pulse perturbation of smallamplitude pressure and a multilayer sample containing two layers of industrial gel with polydisperse air bubbles. It has been shown that a small content of bubbles (about 0.1 vol. %) in a thin gel layer decreases tenfold or more the amplitude of acoustic waves with frequencies close to the resonance frequency of natural oscillations of bubbles. There are frequency ranges thereby where the influence of the bubbly layer is insignificant.

Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal

PubMed Central

2015-01-01

Several competing aetiologies of developmental dyslexia suggest that the problems with acquiring literacy skills are causally entailed by low-level auditory and/or speech perception processes. The purpose of this study is to evaluate the diverging claims about the specific deficient peceptual processes under conditions of strong inference. Theoretically relevant acoustic features were extracted from a set of artificial speech stimuli that lie on a /bAk/-/dAk/ continuum. The features were tested on their ability to enable a simple classifier (Quadratic Discriminant Analysis) to reproduce the observed classification performance of average and dyslexic readers in a speech perception experiment. The ‘classical’ features examined were based on component process accounts of developmental dyslexia such as the supposed deficit in Envelope Rise Time detection and the deficit in the detection of rapid changes in the distribution of energy in the frequency spectrum (formant transitions). Studies examining these temporal processing deficit hypotheses do not employ measures that quantify the temporal dynamics of stimuli. It is shown that measures based on quantification of the dynamics of complex, interaction-dominant systems (Recurrence Quantification Analysis and the multifractal spectrum) enable QDA to classify the stimuli almost identically as observed in dyslexic and average reading participants. It seems unlikely that participants used any of the features that are traditionally associated with accounts of (impaired) speech perception. The nature of the variables quantifying the temporal dynamics of the speech stimuli imply that the classification of speech stimuli cannot be regarded as a linear aggregate of component processes that each parse the acoustic signal independent of one another, as is assumed by the ‘classical’ aetiologies of developmental dyslexia. It is suggested that the results imply that the differences in speech perception performance between

Suppressed Alpha Oscillations Predict Intelligibility of Speech and its Acoustic Details

PubMed Central

Weisz, Nathan

2012-01-01

Modulations of human alpha oscillations (8–13 Hz) accompany many cognitive processes, but their functional role in auditory perception has proven elusive: Do oscillatory dynamics of alpha reflect acoustic details of the speech signal and are they indicative of comprehension success? Acoustically presented words were degraded in acoustic envelope and spectrum in an orthogonal design, and electroencephalogram responses in the frequency domain were analyzed in 24 participants, who rated word comprehensibility after each trial. First, the alpha power suppression during and after a degraded word depended monotonically on spectral and, to a lesser extent, envelope detail. The magnitude of this alpha suppression exhibited an additional and independent influence on later comprehension ratings. Second, source localization of alpha suppression yielded superior parietal, prefrontal, as well as anterior temporal brain areas. Third, multivariate classification of the time–frequency pattern across participants showed that patterns of late posterior alpha power allowed best for above-chance classification of word intelligibility. Results suggest that both magnitude and topography of late alpha suppression in response to single words can indicate a listener's sensitivity to acoustic features and the ability to comprehend speech under adverse listening conditions. PMID:22100354

Seizure classification in EEG signals utilizing Hilbert-Huang transform

PubMed Central

2011-01-01

Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Method Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. Results The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. Conclusion An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing

Seizure classification in EEG signals utilizing Hilbert-Huang transform.

PubMed

Oweis, Rami J; Abdulhay, Enas W

2011-05-24

Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted

Dual fiber Bragg gratings configuration-based fiber acoustic sensor for low-frequency signal detection

NASA Astrophysics Data System (ADS)

Yang, Dong; Wang, Shun; Lu, Ping; Liu, Deming

2014-11-01

We propose and fabricate a new type fiber acoustic sensor based on dual fiber Bragg gratings (FBGs) configuration. The acoustic sensor head is constructed by putting the sensing cells enclosed in an aluminum cylinder space built by two Cband FBGs and a titanium diaphragm of 50 um thickness. One end of each FBG is longitudinally adhered to the diaphragm by UV glue. Both of the two FBGs are employed for reflecting light. The dual FBGs play roles not only as signal transmission system but also as sensing component, and they demodulate each other's optical signal mutually during the measurement. Both of the two FBGs are pre-strained and the output optical power experiences fluctuation in a linear relationship along with a variation of axial strain and surrounding acoustic interference. So a precise approach to measure the frequency and sound pressure of the acoustic disturbance is achieved. Experiments are performed and results show that a relatively flat frequency response in a range from 200 Hz to 1 kHz with the average signal-to-noise ratio (SNR) above 21 dB is obtained. The maximum sound pressure sensitivity of 11.35mV/Pa is achieved with the Rsquared value of 0.99131 when the sound pressure in the range of 87.7-106.6dB. It has potential applications in low frequency signal detection. Owing to its direct self-demodulation method, the sensing system reveals the advantages of easy to demodulate, good temperature stability and measurement reliability. Besides, performance of the proposed sensor could be improved by optimizing the parameters of the sensor, especially the diaphragm.

Active acoustic classification via transient resonance scattering

NASA Astrophysics Data System (ADS)

Gaunaurd, Guillermo C.

1992-12-01

The echoes reflected by a sound ping emerging from active sonar when it interacts with a target in its path can be remotely sensed by a receiver. The presented approach capitalizes on an air inverse scattering method that exploits the presence of certain resonance features in these echoes returned by targets to classify them. Classifying underwater objects is important to naval programs such as mine countermeasures (MC) and anti-submarine warfare (ASW) to preclude wasting of ordnance on false targets. Although the classification of complex shapes is still a formidable task, considerable progress has been made in classifying simple shapes such as spheroidal or cylindrical shells. The briefly overviewed methodology has emphasized the extraction, isolation, and labeling of resonance features hidden within the echo, but little has been said about how these could be used to classify a target. A couple of simple examples illustrate exactly how these resonances can be linked to the physical characteristics of the target, allowing for its unambiguous characterization. The procedure, although illustrated with active acoustics (i.e., sonar), can be extended to any active return from any sensor, including radar.

Controlling a human-computer interface system with a novel classification method that uses electrooculography signals.

PubMed

Wu, Shang-Lin; Liao, Lun-De; Lu, Shao-Wei; Jiang, Wei-Ling; Chen, Shi-An; Lin, Chin-Teng

2013-08-01

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.

Surface Electromyography Signal Processing and Classification Techniques

PubMed Central

Chowdhury, Rubana H.; Reaz, Mamun B. I.; Ali, Mohd Alauddin Bin Mohd; Bakar, Ashrif A. A.; Chellappan, Kalaivani; Chang, Tae. G.

2013-01-01

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. PMID:24048337

Modern Techniques in Acoustical Signal and Image Processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Candy, J V

2002-04-04

Acoustical signal processing problems can lead to some complex and intricate techniques to extract the desired information from noisy, sometimes inadequate, measurements. The challenge is to formulate a meaningful strategy that is aimed at performing the processing required even in the face of uncertainties. This strategy can be as simple as a transformation of the measured data to another domain for analysis or as complex as embedding a full-scale propagation model into the processor. The aims of both approaches are the same--to extract the desired information and reject the extraneous, that is, develop a signal processing scheme to achieve thismore » goal. In this paper, we briefly discuss this underlying philosophy from a ''bottom-up'' approach enabling the problem to dictate the solution rather than visa-versa.« less

Automatic classification of sleep stages based on the time-frequency image of EEG signals.

PubMed

Bajaj, Varun; Pachori, Ram Bilas

2013-12-01

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Turboprop and rotary-wing aircraft flight parameter estimation using both narrow-band and broadband passive acoustic signal-processing methods.

PubMed

Ferguson, B G; Lo, K W

2000-10-01

Flight parameter estimation methods for an airborne acoustic source can be divided into two categories, depending on whether the narrow-band lines or the broadband component of the received signal spectrum is processed to estimate the flight parameters. This paper provides a common framework for the formulation and test of two flight parameter estimation methods: one narrow band, the other broadband. The performances of the two methods are evaluated by applying them to the same acoustic data set, which is recorded by a planar array of passive acoustic sensors during multiple transits of a turboprop fixed-wing aircraft and two types of rotary-wing aircraft. The narrow-band method, which is based on a kinematic model that assumes the source travels in a straight line at constant speed and altitude, requires time-frequency analysis of the acoustic signal received by a single sensor during each aircraft transit. The broadband method is based on the same kinematic model, but requires observing the temporal variation of the differential time of arrival of the acoustic signal at each pair of sensors that comprises the planar array. Generalized cross correlation of each pair of sensor outputs using a cross-spectral phase transform prefilter provides instantaneous estimates of the differential times of arrival of the signal as the acoustic wavefront traverses the array.

A methodology to condition distorted acoustic emission signals to identify fracture timing from human cadaver spine impact tests.

PubMed

Arun, Mike W J; Yoganandan, Narayan; Stemper, Brian D; Pintar, Frank A

2014-12-01

While studies have used acoustic sensors to determine fracture initiation time in biomechanical studies, a systematic procedure is not established to process acoustic signals. The objective of the study was to develop a methodology to condition distorted acoustic emission data using signal processing techniques to identify fracture initiation time. The methodology was developed from testing a human cadaver lumbar spine column. Acoustic sensors were glued to all vertebrae, high-rate impact loading was applied, load-time histories were recorded (load cell), and fracture was documented using CT. Compression fracture occurred to L1 while other vertebrae were intact. FFT of raw voltage-time traces were used to determine an optimum frequency range associated with high decibel levels. Signals were bandpass filtered in this range. Bursting pattern was found in the fractured vertebra while signals from other vertebrae were silent. Bursting time was associated with time of fracture initiation. Force at fracture was determined using this time and force-time data. The methodology is independent of selecting parameters a priori such as fixing a voltage level(s), bandpass frequency and/or using force-time signal, and allows determination of force based on time identified during signal processing. The methodology can be used for different body regions in cadaver experiments. Copyright © 2014 Elsevier Ltd. All rights reserved.

Aircraft Operations Classification System

NASA Technical Reports Server (NTRS)

Harlow, Charles; Zhu, Weihong

2001-01-01

Accurate data is important in the aviation planning process. In this project we consider systems for measuring aircraft activity at airports. This would include determining the type of aircraft such as jet, helicopter, single engine, and multiengine propeller. Some of the issues involved in deploying technologies for monitoring aircraft operations are cost, reliability, and accuracy. In addition, the system must be field portable and acceptable at airports. A comparison of technologies was conducted and it was decided that an aircraft monitoring system should be based upon acoustic technology. A multimedia relational database was established for the study. The information contained in the database consists of airport information, runway information, acoustic records, photographic records, a description of the event (takeoff, landing), aircraft type, and environmental information. We extracted features from the time signal and the frequency content of the signal. A multi-layer feed-forward neural network was chosen as the classifier. Training and testing results were obtained. We were able to obtain classification results of over 90 percent for training and testing for takeoff events.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

PubMed

Subasi, Abdulhamit

2013-06-01

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. Copyright © 2013 Elsevier Ltd. All rights reserved.

A support vector machine approach for classification of welding defects from ultrasonic signals

NASA Astrophysics Data System (ADS)

Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

2014-07-01

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

Acoustic signals in the sand fly Lutzomyia (Nyssomyia) intermedia (Diptera: Psychodidae).

PubMed

Vigoder, Felipe M; Souza, Nataly A; Peixoto, Alexandre A

2011-05-13

Acoustic signals are part of the courtship of many insects and they often act as species-specific signals that are important in the reproductive isolation of closely related species. Here we report the courtship songs of the sand fly Lutzomyia (Nyssomyia) intermedia, one of the main vectors of cutaneous leishmaniasis in Brazil. Recordings were performed using insects from three localities from Eastern Brazil: Posse and Jacarepaguá in Rio de Janeiro State and Corte de Pedra in Bahia State. The three areas have remnants of the Brazilian Atlantic forest, they are endemic for cutaneous leishmaniasis and L. intermedia is the predominant sand fly species. We observed that during courtship L. intermedia males from all populations produced pulse songs consisting of short trains. No significant differences in song parameters were observed between the males of the three localities. L. intermedia males produce acoustic signals as reported for some other sand flies such as the sibling species of the Lutzomyia longipalpis complex. The lack of differences between the males from the three localities is consistent with previous molecular studies of the period gene carried out in the same populations, reinforcing the idea that L. intermedia is not a species complex in the studied areas and that the three populations are likely to have similar vectorial capacities.

Digital signal processing at Bell Labs-Foundations for speech and acoustics research

NASA Astrophysics Data System (ADS)

Rabiner, Lawrence R.

2004-05-01

Digital signal processing (DSP) is a fundamental tool for much of the research that has been carried out of Bell Labs in the areas of speech and acoustics research. The fundamental bases for DSP include the sampling theorem of Nyquist, the method for digitization of analog signals by Shannon et al., methods of spectral analysis by Tukey, the cepstrum by Bogert et al., and the FFT by Tukey (and Cooley of IBM). Essentially all of these early foundations of DSP came out of the Bell Labs Research Lab in the 1930s, 1940s, 1950s, and 1960s. This fundamental research was motivated by fundamental applications (mainly in the areas of speech, sonar, and acoustics) that led to novel design methods for digital filters (Kaiser, Golden, Rabiner, Schafer), spectrum analysis methods (Rabiner, Schafer, Allen, Crochiere), fast convolution methods based on the FFT (Helms, Bergland), and advanced digital systems used to implement telephony channel banks (Jackson, McDonald, Freeny, Tewksbury). This talk summarizes the key contributions to DSP made at Bell Labs, and illustrates how DSP was utilized in the areas of speech and acoustics research. It also shows the vast, worldwide impact of this DSP research on modern consumer electronics.

Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks.

PubMed

Dura-Bernal, Salvador; Garreau, Guillaume; Georgiou, Julius; Andreou, Andreas G; Denham, Susan L; Wennekers, Thomas

2013-10-01

The ability to recognize the behavior of individuals is of great interest in the general field of safety (e.g. building security, crowd control, transport analysis, independent living for the elderly). Here we report a new real-time acoustic system for human action and behavior recognition that integrates passive audio and active micro-Doppler sonar signatures over multiple time scales. The system architecture is based on a six-layer convolutional neural network, trained and evaluated using a dataset of 10 subjects performing seven different behaviors. Probabilistic combination of system output through time for each modality separately yields 94% (passive audio) and 91% (micro-Doppler sonar) correct behavior classification; probabilistic multimodal integration increases classification performance to 98%. This study supports the efficacy of micro-Doppler sonar systems in characterizing human actions, which can then be efficiently classified using ConvNets. It also demonstrates that the integration of multiple sources of acoustic information can significantly improve the system's performance.

Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

PubMed Central

Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Saad, Fathinul Syahir Ahmad; Adom, Abdul Hamid; Ahmad, Mohd Noor; Jaafar, Mahmad Nor; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2012-01-01

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied. PMID:22778629

Improved maturity and ripeness classifications of Magnifera Indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor.

PubMed

Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Saad, Fathinul Syahir Ahmad; Adom, Abdul Hamid; Ahmad, Mohd Noor; Jaafar, Mahmad Nor; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2012-01-01

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.

Acoustic effects of the ATOC signal (75 Hz, 195 dB) on dolphins and whales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Au, W.W.; Nachtigall, P.E.; Pawloski, J.L.

1997-05-01

The Acoustic Thermometry of Ocean Climate (ATOC) program of Scripps Institution of Oceanography and the Applied Physics Laboratory, University of Washington, will broadcast a low-frequency 75-Hz phase modulated acoustic signal over ocean basins in order to study ocean temperatures on a global scale and examine the effects of global warming. One of the major concerns is the possible effect of the ATOC signal on marine life, especially on dolphins and whales. In order to address this issue, the hearing sensitivity of a false killer whale ({ital Pseudorca crassidens}) and a Risso{close_quote}s dolphin ({ital Grampus griseus}) to the ATOC sound wasmore » measured behaviorally. A staircase procedure with the signal levels being changed in 1-dB steps was used to measure the animals{close_quote} threshold to the actual ATOC coded signal. The results indicate that small odontocetes such as the {ital Pseudorca} and {ital Grampus} swimming directly above the ATOC source will not hear the signal unless they dive to a depth of approximately 400 m. A sound propagation analysis suggests that the sound-pressure level at ranges greater than 0.5 km will be less than 130 dB for depths down to about 500 m. Several species of baleen whales produce sounds much greater than 170{endash}180 dB. With the ATOC source on the axis of the deep sound channel (greater than 800 m), the ATOC signal will probably have minimal physical and physiological effects on cetaceans. {copyright} {ital 1997 Acoustical Society of America.}« less

Acoustic emission signal processing for rolling bearing running state assessment using compressive sensing

NASA Astrophysics Data System (ADS)

Liu, Chang; Wu, Xing; Mao, Jianlin; Liu, Xiaoqin

2017-07-01

In the signal processing domain, there has been growing interest in using acoustic emission (AE) signals for the fault diagnosis and condition assessment instead of vibration signals, which has been advocated as an effective technique for identifying fracture, crack or damage. The AE signal has high frequencies up to several MHz which can avoid some signals interference, such as the parts of bearing (i.e. rolling elements, ring and so on) and other rotating parts of machine. However, acoustic emission signal necessitates advanced signal sampling capabilities and requests ability to deal with large amounts of sampling data. In this paper, compressive sensing (CS) is introduced as a processing framework, and then a compressive features extraction method is proposed. We use it for extracting the compressive features from compressively-sensed data directly, and also prove the energy preservation properties. First, we study the AE signals under the CS framework. The sparsity of AE signal of the rolling bearing is checked. The observation and reconstruction of signal is also studied. Second, we present a method of extraction AE compressive feature (AECF) from compressively-sensed data directly. We demonstrate the energy preservation properties and the processing of the extracted AECF feature. We assess the running state of the bearing using the AECF trend. The AECF trend of the running state of rolling bearings is consistent with the trend of traditional features. Thus, the method is an effective way to evaluate the running trend of rolling bearings. The results of the experiments have verified that the signal processing and the condition assessment based on AECF is simpler, the amount of data required is smaller, and the amount of computation is greatly reduced.

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

NASA Technical Reports Server (NTRS)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Multiple Signal Classification for Gravitational Wave Burst Search

NASA Astrophysics Data System (ADS)

Cao, Junwei; He, Zhengqi

2013-01-01

This work is mainly focused on the application of the multiple signal classification (MUSIC) algorithm for gravitational wave burst search. This algorithm extracts important gravitational wave characteristics from signals coming from detectors with arbitrary position, orientation and noise covariance. In this paper, the MUSIC algorithm is described in detail along with the necessary adjustments required for gravitational wave burst search. The algorithm's performance is measured using simulated signals and noise. MUSIC is compared with the Q-transform for signal triggering and with Bayesian analysis for direction of arrival (DOA) estimation, using the Ω-pipeline. Experimental results show that MUSIC has a lower resolution but is faster. MUSIC is a promising tool for real-time gravitational wave search for multi-messenger astronomy.

Acoustical standards in engineering acoustics

NASA Astrophysics Data System (ADS)

Burkhard, Mahlon D.

2004-05-01

The Engineering Acoustics Technical Committee is concerned with the evolution and improvement of acoustical techniques and apparatus, and with the promotion of new applications of acoustics. As cited in the Membership Directory and Handbook (2002), the interest areas include transducers and arrays; underwater acoustic systems; acoustical instrumentation and monitoring; applied sonics, promotion of useful effects, information gathering and transmission; audio engineering; acoustic holography and acoustic imaging; acoustic signal processing (equipment and techniques); and ultrasound and infrasound. Evident connections between engineering and standards are needs for calibration, consistent terminology, uniform presentation of data, reference levels, or design targets for product development. Thus for the acoustical engineer standards are both a tool for practices, for communication, and for comparison of his efforts with those of others. Development of many standards depends on knowledge of the way products are put together for the market place and acoustical engineers provide important input to the development of standards. Acoustical engineers and members of the Engineering Acoustics arm of the Society both benefit from and contribute to the Acoustical Standards of the Acoustical Society.

A scattering analysis of echoes due to biosonar signals emitted by foraging beaked whales

NASA Astrophysics Data System (ADS)

Jones, Benjamin A.; Stanton, Timothy K.; Lavery, Andone C.; Johnson, Mark P.; Madsen, Peter T.; Tyack, Peter L.

2005-09-01

Blainville's beaked whales (Mesoplodon densirostris) hunt their prey by echolocation at depths of more than 500 meters. These whales use a FM upswept, ultrasonic click, of greater than an octave bandwidth to search for, localize, and close on individual prey which generally consist of mesopelagic fishes and squid. It is well known that acoustic scattering from organisms of varying morphology (e.g., swimbladder-bearing or fluidlike) is strongly frequency dependent. However, it is unknown if the broadband nature of the whales' outgoing signal, and the frequency dependence of the echoes, is a key component in the classification and selection of their prey. Non-invasive, acoustic ``Dtags,'' which sample stereo acoustic data at a rate which satisfies the high-frequency Nyquist criterion for the animal's transmit signal, were affixed to beaked whales. The Dtags successfully recorded transmitted signals and associated echoes. Structure was observed in the frequency content of echoes from isolated targets in the water column which may be used for classification by the whales. An analysis of the echoes identified as possibly due to prey has demonstrated that multiple classes of frequency responses are present. These results will be compared with the frequency responses of possible prey types.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Classification of Acousto-Optic Correlation Signatures of Spread Spectrum Signals Using Artificial Neural Networks

DTIC Science & Technology

1989-12-01

Ohio ’aPw iorlipuab muo i 0I2, AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL...ENG/89D- 10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL NEURAL NETWORKS THESIS John W. DeBerry...Captain, USAF AFIT/GE/ENG/89D- 10 Approved for public release; distribution unlimited. AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION

The potential influence of morphology on the evolutionary divergence of an acoustic signal

PubMed Central

Pitchers, W. R.; Klingenberg, C.P.; Tregenza, Tom; Hunt, J.; Dworkin, I.

2014-01-01

The evolution of acoustic behaviour and that of the morphological traits mediating its production are often coupled. Lack of variation in the underlying morphology of signalling traits has the potential to constrain signal evolution. This relationship is particularly likely in field crickets, where males produce acoustic advertisement signals to attract females by stridulating with specialized structures on their forewings. In this study, we characterise the size and geometric shape of the forewings of males from six allopatric populations of the black field cricket (Teleogryllus commodus) known to have divergent advertisement calls. We sample from each of these populations using both wild-caught and common-garden reared cohorts, allowing us to test for multivariate relationships between wing morphology and call structure. We show that the allometry of shape has diverged across populations. However, there was a surprisingly small amount of covariation between wing shape and call structure within populations. Given the importance of male size for sexual selection in crickets, the divergence we observe among populations has the potential to influence the evolution of advertisement calls in this species. PMID:25223712

A novel deep learning approach for classification of EEG motor imagery signals.

PubMed

Tabar, Yousef Rezaei; Halici, Ugur

2017-02-01

Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

System And Method For Characterizing Voiced Excitations Of Speech And Acoustic Signals, Removing Acoustic Noise From Speech, And Synthesizi

DOEpatents

Burnett, Greg C.; Holzrichter, John F.; Ng, Lawrence C.

2006-04-25

The present invention is a system and method for characterizing human (or animate) speech voiced excitation functions and acoustic signals, for removing unwanted acoustic noise which often occurs when a speaker uses a microphone in common environments, and for synthesizing personalized or modified human (or other animate) speech upon command from a controller. A low power EM sensor is used to detect the motions of windpipe tissues in the glottal region of the human speech system before, during, and after voiced speech is produced by a user. From these tissue motion measurements, a voiced excitation function can be derived. Further, the excitation function provides speech production information to enhance noise removal from human speech and it enables accurate transfer functions of speech to be obtained. Previously stored excitation and transfer functions can be used for synthesizing personalized or modified human speech. Configurations of EM sensor and acoustic microphone systems are described to enhance noise cancellation and to enable multiple articulator measurements.

Fatigue crack localization with near-field acoustic emission signals

NASA Astrophysics Data System (ADS)

Zhou, Changjiang; Zhang, Yunfeng

2013-04-01

This paper presents an AE source localization technique using near-field acoustic emission (AE) signals induced by crack growth and propagation. The proposed AE source localization technique is based on the phase difference in the AE signals measured by two identical AE sensing elements spaced apart at a pre-specified distance. This phase difference results in canceling-out of certain frequency contents of signals, which can be related to AE source direction. Experimental data from simulated AE source such as pencil breaks was used along with analytical results from moment tensor analysis. It is observed that the theoretical predictions, numerical simulations and the experimental test results are in good agreement. Real data from field monitoring of an existing fatigue crack on a bridge was also used to test this system. Results show that the proposed method is fairly effective in determining the AE source direction in thick plates commonly encountered in civil engineering structures.

Elevated stress hormone diminishes the strength of female preferences for acoustic signals in the green treefrog.

PubMed

Davis, A Gabriell; Leary, Christopher J

2015-03-01

Mate selection can be stressful; time spent searching for mates can increase predation risk and/or decrease food consumption, resulting in elevated stress hormone levels. Both high predation risk and low food availability are often associated with increased variation in mate choice by females, but it is not clear whether stress hormone levels contribute to such variation in female behavior. We examined how the stress hormone corticosterone (CORT) affects female preferences for acoustic signals in the green treefrog, Hyla cinerea. Specifically, we assessed whether CORT administration affects female preferences for call rate - an acoustic feature that is typically under directional selection via mate choice by females in most anurans and other species that communicate using acoustic signals. Using a dual speaker playback paradigm, we show that females that were administered higher doses of CORT were less likely to choose male advertisement calls broadcast at high rates. Neither CORT dose nor level was related to the latency of female phonotactic responses, suggesting that elevated CORT does not influence the motivation to mate. Results were also not related to circulating sex steroids (i.e., progesterone, androgens or estradiol) that have traditionally been the focus of studies examining the hormonal basis for variation in female mate choice. Our results thus indicate that elevated CORT levels decrease the strength of female preferences for acoustic signals. Copyright © 2015 Elsevier Inc. All rights reserved.

A multivariate analytical method to characterize sediment attributes from high-frequency acoustic backscatter and ground-truthing data (Jade Bay, German North Sea coast)

NASA Astrophysics Data System (ADS)

Biondo, Manuela; Bartholomä, Alexander

2017-04-01

One of the burning issues on the topic of acoustic seabed classification is the lack of solid, repeatable, statistical procedures that can support the verification of acoustic variability in relation to seabed properties. Acoustic sediment classification schemes often lead to biased and subjective interpretation, as they ultimately aim at an oversimplified categorization of the seabed based on conventionally defined sediment types. However, grain size variability alone cannot be accounted for acoustic diversity, which will be ultimately affected by multiple physical processes, scale of heterogeneity, instrument settings, data quality, image processing and segmentation performances. Understanding and assessing the weight of all of these factors on backscatter is a difficult task, due to the spatially limited and fragmentary knowledge of the seabed from of direct observations (e.g. grab samples, cores, videos). In particular, large-scale mapping requires an enormous availability of ground-truthing data that is often obtained from heterogeneous and multidisciplinary sources, resulting into a further chance of misclassification. Independently from all of these limitations, acoustic segments still contain signals for seabed changes that, if appropriate procedures are established, can be translated into meaningful knowledge. In this study we design a simple, repeatable method, based on multivariate procedures, with the scope to classify a 100 km2, high-frequency (450 kHz) sidescan sonar mosaic acquired in the year 2012 in the shallow upper-mesotidal inlet of the Jade Bay (German North Sea coast). The tool used for the automated classification of the backscatter mosaic is the QTC SWATHVIEWTMsoftware. The ground-truthing database included grab sample data from multiple sources (2009-2011). The method was designed to extrapolate quantitative descriptors for acoustic backscatter and model their spatial changes in relation to grain size distribution and morphology. The

Acoustic signals in the sand fly Lutzomyia (Nyssomyia) intermedia (Diptera: Psychodidae)

PubMed Central

2011-01-01

Background Acoustic signals are part of the courtship of many insects and they often act as species-specific signals that are important in the reproductive isolation of closely related species. Here we report the courtship songs of the sand fly Lutzomyia (Nyssomyia) intermedia, one of the main vectors of cutaneous leishmaniasis in Brazil. Findings Recordings were performed using insects from three localities from Eastern Brazil: Posse and Jacarepaguá in Rio de Janeiro State and Corte de Pedra in Bahia State. The three areas have remnants of the Brazilian Atlantic forest, they are endemic for cutaneous leishmaniasis and L. intermedia is the predominant sand fly species. We observed that during courtship L. intermedia males from all populations produced pulse songs consisting of short trains. No significant differences in song parameters were observed between the males of the three localities. Conclusions L. intermedia males produce acoustic signals as reported for some other sand flies such as the sibling species of the Lutzomyia longipalpis complex. The lack of differences between the males from the three localities is consistent with previous molecular studies of the period gene carried out in the same populations, reinforcing the idea that L. intermedia is not a species complex in the studied areas and that the three populations are likely to have similar vectorial capacities. PMID:21569534

The acoustic repertoire and behavioural context of the vocalisations of a nocturnal dasyurid, the eastern quoll (Dasyurus viverrinus)

PubMed Central

Dorph, Annalie

2017-01-01

Defining an acoustic repertoire is essential to understanding vocal signalling and communicative interactions within a species. Currently, quantitative and statistical definition is lacking for the vocalisations of many dasyurids, an important group of small to medium-sized marsupials from Australasia that includes the eastern quoll (Dasyurus viverrinus), a species of conservation concern. Beyond generating a better understanding of this species' social interactions, determining an acoustic repertoire will further improve detection rates and inference of vocalisations gathered by automated bioacoustic recorders. Hence, this study investigated eastern quoll vocalisations using objective signal processing techniques to quantitatively analyse spectrograms recorded from 15 different individuals. Recordings were collected in conjunction with observations of the behaviours associated with each vocalisation to develop an acoustic-based behavioural repertoire for the species. Analysis of recordings produced a putative classification of five vocalisation types: Bark, Growl, Hiss, Cp-cp, and Chuck. These were most frequently observed during agonistic encounters between conspecifics, most likely as a graded sequence from Hisses occurring in a warning context through to Growls and finally Barks being given prior to, or during, physical confrontations between individuals. Quantitative and statistical methods were used to objectively establish the accuracy of these five putative call types. A multinomial logistic regression indicated a 97.27% correlation with the perceptual classification, demonstrating support for the five different vocalisation types. This putative classification was further supported by hierarchical cluster analysis and silhouette information that determined the optimal number of clusters to be five. Minor disparity between the objective and perceptual classifications was potentially the result of gradation between vocalisations, or subtle differences present

Real-time implementations of acoustic signal enhancement techniques for aerial based surveillance and rescue applications

NASA Astrophysics Data System (ADS)

Ramos, Antonio L. L.; Shao, Zhili; Holthe, Aleksander; Sandli, Mathias F.

2017-05-01

The introduction of the System-on-Chip (SoC) technology has brought exciting new opportunities for the development of smart low cost embedded systems spanning a wide range of applications. Currently available SoC devices are capable of performing high speed digital signal processing tasks in software while featuring relatively low development costs and reduced time-to-market. Unmanned aerial vehicles (UAV) are an application example that has shown tremendous potential in an increasing number of scenarios, ranging from leisure to surveillance as well as in search and rescue missions. Video capturing from UAV platforms is a relatively straightforward task that requires almost no preprocessing. However, that does not apply to audio signals, especially in cases where the data is to be used to support real-time decision making. In fact, the enormous amount of acoustic interference from the surroundings, including the noise from the UAVs propellers, becomes a huge problem. This paper discusses a real-time implementation of the NLMS adaptive filtering algorithm applied to enhancing acoustic signals captured from UAV platforms. The model relies on a combination of acoustic sensors and a computational inexpensive algorithm running on a digital signal processor. Given its simplicity, this solution can be incorporated into the main processing system of an UAV using the SoC technology, and run concurrently with other required tasks, such as flight control and communications. Simulations and real-time DSP-based implementations have shown significant signal enhancement results by efficiently mitigating the interference from the noise generated by the UAVs propellers as well as from other external noise sources.

ICA-Based Imagined Conceptual Words Classification on EEG Signals.

PubMed

Imani, Ehsan; Pourmohammad, Ali; Bagheri, Mahsa; Mobasheri, Vida

2017-01-01

Independent component analysis (ICA) has been used for detecting and removing the eye artifacts conventionally. However, in this research, it was used not only for detecting the eye artifacts, but also for detecting the brain-produced signals of two conceptual danger and information category words. In this cross-sectional research, electroencephalography (EEG) signals were recorded using Micromed and 19-channel helmet devices in unipolar mode, wherein Cz electrode was selected as the reference electrode. In the first part of this research, the statistical community test case included four men and four women, who were 25-30 years old. In the designed task, three groups of traffic signs were considered, in which two groups referred to the concept of danger, and the third one referred to the concept of information. In the second part, the three volunteers, two men and one woman, who had the best results, were chosen from among eight participants. In the second designed task, direction arrows (up, down, left, and right) were used. For the 2/8 volunteers in the rest times, very high-power alpha waves were observed from the back of the head; however, in the thinking times, they were different. According to this result, alpha waves for changing the task from thinking to rest condition took at least 3 s for the two volunteers, and it was at most 5 s until they went to the absolute rest condition. For the 7/8 volunteers, the danger and information signals were well classified; these differences for the 5/8 volunteers were observed in the right hemisphere, and, for the other three volunteers, the differences were observed in the left hemisphere. For the second task, simulations showed that the best classification accuracies resulted when the time window was 2.5 s. In addition, it also showed that the features of the autoregressive (AR)-15 model coefficients were the best choices for extracting the features. For all the states of neural network except hardlim discriminator

Department of Cybernetic Acoustics

NASA Astrophysics Data System (ADS)

The development of the theory, instrumentation and applications of methods and systems for the measurement, analysis, processing and synthesis of acoustic signals within the audio frequency range, particularly of the speech signal and the vibro-acoustic signal emitted by technical and industrial equipments treated as noise and vibration sources was discussed. The research work, both theoretical and experimental, aims at applications in various branches of science, and medicine, such as: acoustical diagnostics and phoniatric rehabilitation of pathological and postoperative states of the speech organ; bilateral ""man-machine'' speech communication based on the analysis, recognition and synthesis of the speech signal; vibro-acoustical diagnostics and continuous monitoring of the state of machines, technical equipments and technological processes.

Classification of vocal aging using parameters extracted from the glottal signal.

PubMed

Forero Mendoza, Leonardo A; Cataldo, Edson; Vellasco, Marley M B R; Silva, Marco A; Apolinário, José A

2014-09-01

This article proposes and evaluates a method to classify vocal aging using artificial neural network (ANN) and support vector machine (SVM), using the parameters extracted from the speech signal as inputs. For each recorded speech, from a corpus of male and female speakers of different ages, the corresponding glottal signal is obtained using an inverse filtering algorithm. The Mel Frequency Cepstrum Coefficients (MFCC) also extracted from the voice signal and the features extracted from the glottal signal are supplied to an ANN and an SVM with a previous selection. The selection is performed by a wrapper approach of the most relevant parameters. Three groups are considered for the aging-voice classification: young (aged 15-30 years), adult (aged 31-60 years), and senior (aged 61-90 years). The results are compared using different possibilities: with only the parameters extracted from the glottal signal, with only the MFCC, and with a combination of both. The results demonstrate that the best classification rate is obtained using the glottal signal features, which is a novel result and the main contribution of this article. Copyright © 2014 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.

PubMed

Li, Mi; Xu, Hongpei; Liu, Xingwang; Lu, Shengfu

2018-04-27

Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

Optimal Methods for Classification of Digitally Modulated Signals

DTIC Science & Technology

2013-03-01

of using a ratio of likelihood functions, the proposed approach uses the Kullback - Leibler (KL) divergence. KL...58 List of Acronyms ALRT Average LRT BPSK Binary Shift Keying BPSK-SS BPSK Spread Spectrum or CDMA DKL Kullback - Leibler Information Divergence...blind demodulation for develop classification algorithms for wider set of signals types. Two methodologies were used : Likelihood Ratio Test

Acoustic emission from single point machining: Part 2, Signal changes with tool wear

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.

1989-01-01

Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

Efficient source separation algorithms for acoustic fall detection using a microsoft kinect.

PubMed

Li, Yun; Ho, K C; Popescu, Mihail

2014-03-01

Falls have become a common health problem among older adults. In previous study, we proposed an acoustic fall detection system (acoustic FADE) that employed a microphone array and beamforming to provide automatic fall detection. However, the previous acoustic FADE had difficulties in detecting the fall signal in environments where interference comes from the fall direction, the number of interferences exceeds FADE's ability to handle or a fall is occluded. To address these issues, in this paper, we propose two blind source separation (BSS) methods for extracting the fall signal out of the interferences to improve the fall classification task. We first propose the single-channel BSS by using nonnegative matrix factorization (NMF) to automatically decompose the mixture into a linear combination of several basis components. Based on the distinct patterns of the bases of falls, we identify them efficiently and then construct the interference free fall signal. Next, we extend the single-channel BSS to the multichannel case through a joint NMF over all channels followed by a delay-and-sum beamformer for additional ambient noise reduction. In our experiments, we used the Microsoft Kinect to collect the acoustic data in real-home environments. The results show that in environments with high interference and background noise levels, the fall detection performance is significantly improved using the proposed BSS approaches.

Measures of voiced frication for automatic classification

NASA Astrophysics Data System (ADS)

Jackson, Philip J. B.; Jesus, Luis M. T.; Shadle, Christine H.; Pincas, Jonathan

2004-05-01

As an approach to understanding the characteristics of the acoustic sources in voiced fricatives, it seems apt to draw on knowledge of vowels and voiceless fricatives, which have been relatively well studied. However, the presence of both phonation and frication in these mixed-source sounds offers the possibility of mutual interaction effects, with variations across place of articulation. This paper examines the acoustic and articulatory consequences of these interactions and explores automatic techniques for finding parametric and statistical descriptions of these phenomena. A reliable and consistent set of such acoustic cues could be used for phonetic classification or speech recognition. Following work on devoicing of European Portuguese voiced fricatives [Jesus and Shadle, in Mamede et al. (eds.) (Springer-Verlag, Berlin, 2003), pp. 1-8]. and the modulating effect of voicing on frication [Jackson and Shadle, J. Acoust. Soc. Am. 108, 1421-1434 (2000)], the present study focuses on three types of information: (i) sequences and durations of acoustic events in VC transitions, (ii) temporal, spectral and modulation measures from the periodic and aperiodic components of the acoustic signal, and (iii) voicing activity derived from simultaneous EGG data. Analysis of interactions observed in British/American English and European Portuguese speech corpora will be compared, and the principal findings discussed.

Multiple Signal Classification for Determining Direction of Arrival of Frequency Hopping Spread Spectrum Signals

DTIC Science & Technology

2014-03-27

42 4.2.3 Number of Hops Hs . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2.4 Number of Sensors M... 45 4.5 Standard deviation vs. Ns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.6 Bias...laboratory MTM multiple taper method MUSIC multiple signal classification MVDR minimum variance distortionless reposnse PSK phase shift keying QAM

System and method for characterizing voiced excitations of speech and acoustic signals, removing acoustic noise from speech, and synthesizing speech

DOEpatents

Burnett, Greg C [Livermore, CA; Holzrichter, John F [Berkeley, CA; Ng, Lawrence C [Danville, CA

2006-08-08

The present invention is a system and method for characterizing human (or animate) speech voiced excitation functions and acoustic signals, for removing unwanted acoustic noise which often occurs when a speaker uses a microphone in common environments, and for synthesizing personalized or modified human (or other animate) speech upon command from a controller. A low power EM sensor is used to detect the motions of windpipe tissues in the glottal region of the human speech system before, during, and after voiced speech is produced by a user. From these tissue motion measurements, a voiced excitation function can be derived. Further, the excitation function provides speech production information to enhance noise removal from human speech and it enables accurate transfer functions of speech to be obtained. Previously stored excitation and transfer functions can be used for synthesizing personalized or modified human speech. Configurations of EM sensor and acoustic microphone systems are described to enhance noise cancellation and to enable multiple articulator measurements.

System and method for characterizing voiced excitations of speech and acoustic signals, removing acoustic noise from speech, and synthesizing speech

DOEpatents

Burnett, Greg C.; Holzrichter, John F.; Ng, Lawrence C.

2004-03-23

The present invention is a system and method for characterizing human (or animate) speech voiced excitation functions and acoustic signals, for removing unwanted acoustic noise which often occurs when a speaker uses a microphone in common environments, and for synthesizing personalized or modified human (or other animate) speech upon command from a controller. A low power EM sensor is used to detect the motions of windpipe tissues in the glottal region of the human speech system before, during, and after voiced speech is produced by a user. From these tissue motion measurements, a voiced excitation function can be derived. Further, the excitation function provides speech production information to enhance noise removal from human speech and it enables accurate transfer functions of speech to be obtained. Previously stored excitation and transfer functions can be used for synthesizing personalized or modified human speech. Configurations of EM sensor and acoustic microphone systems are described to enhance noise cancellation and to enable multiple articulator measurements.

System and method for characterizing voiced excitations of speech and acoustic signals, removing acoustic noise from speech, and synthesizing speech

DOEpatents

Burnett, Greg C.; Holzrichter, John F.; Ng, Lawrence C.

2006-02-14

The present invention is a system and method for characterizing human (or animate) speech voiced excitation functions and acoustic signals, for removing unwanted acoustic noise which often occurs when a speaker uses a microphone in common environments, and for synthesizing personalized or modified human (or other animate) speech upon command from a controller. A low power EM sensor is used to detect the motions of windpipe tissues in the glottal region of the human speech system before, during, and after voiced speech is produced by a user. From these tissue motion measurements, a voiced excitation function can be derived. Further, the excitation function provides speech production information to enhance noise removal from human speech and it enables accurate transfer functions of speech to be obtained. Previously stored excitation and transfer functions can be used for synthesizing personalized or modified human speech. Configurations of EM sensor and acoustic microphone systems are described to enhance noise cancellation and to enable multiple articulator measurements.

Seismo-acoustic Signals Recorded at KSIAR, the Infrasound Array Installed at PS31

NASA Astrophysics Data System (ADS)

Kim, T. S.; Che, I. Y.; Jeon, J. S.; Chi, H. C.; Kang, I. B.

2014-12-01

One of International Monitoring System (IMS)'s primary seismic stations, PS31, called Korea Seismic Research Station (KSRS), was installed around Wonju, Korea in 1970s. It has been operated by US Air Force Technical Applications Center (AFTAC) for more than 40 years. KSRS is composed of 26 seismic sensors including 19 short period, 6 long period and 1 broad band seismometers. The 19 short period sensors were used to build an array with a 10-km aperture while the 6 long period sensors were used for a relatively long period array with a 40-km aperture. After KSRS was certified as an IMS station in 2006 by Comprehensive Nuclear Test Ban Treaty Organization (CTBTO), Korea Institute of Geoscience and Mineral Resources (KIGAM) which is the Korea National Data Center started to take over responsibilities on the operation and maintenance of KSRS from AFTAC. In April of 2014, KIGAM installed an infrasound array, KSIAR, on the existing four short period seismic stations of KSRS, the sites KS05, KS06, KS07 and KS16. The collocated KSIAR changed KSRS from a seismic array into a seismo-acoustic array. The aperture of KSIAR is 3.3 km. KSIAR also has a 100-m small aperture infrasound array at KS07. The infrasound data from KSIAR except that from the site KS06 is being transmitted in real time to KIGAM with VPN and internet line. An initial analysis on seismo-acoustic signals originated from local and regional distance ranges has been performed since May 2014. The analysis with the utilization of an array process called Progressive Multi-Channel Correlation (PMCC) detected seismo-acoustic signals caused by various sources including small explosions in relation to constructing local tunnels and roads. Some of them were not found in the list of automatic bulletin of KIGAM. The seismo-acoustic signals recorded by KSIAR are supplying a useful information for discriminating local and regional man-made events from natural events.

Acoustics based assessment of respiratory diseases using GMM classification.

PubMed

Mayorga, P; Druzgalski, C; Morelos, R L; Gonzalez, O H; Vidales, J

2010-01-01

The focus of this paper is to present a method utilizing lung sounds for a quantitative assessment of patient health as it relates to respiratory disorders. In order to accomplish this, applicable traditional techniques within the speech processing domain were utilized to evaluate lung sounds obtained with a digital stethoscope. Traditional methods utilized in the evaluation of asthma involve auscultation and spirometry, but utilization of more sensitive electronic stethoscopes, which are currently available, and application of quantitative signal analysis methods offer opportunities of improved diagnosis. In particular we propose an acoustic evaluation methodology based on the Gaussian Mixed Models (GMM) which should assist in broader analysis, identification, and diagnosis of asthma based on the frequency domain analysis of wheezing and crackles.

Flow velocity profiling using acoustic time of flight flow metering based on wide band signals and adaptive beam-forming techniques

NASA Astrophysics Data System (ADS)

Murgan, I.; Candel, I.; Ioana, C.; Digulescu, A.; Bunea, F.; Ciocan, G. D.; Anghel, A.; Vasile, G.

2016-11-01

In this paper, we present a novel approach to non-intrusive flow velocity profiling technique using multi-element sensor array and wide-band signal's processing methods. Conventional techniques for the measurements of the flow velocity profiles are usually based on intrusive instruments (current meters, acoustic Doppler profilers, Pitot tubes, etc.) that take punctual velocity readings. Although very efficient, these choices are limited in terms of practical cases of applications especially when non-intrusive measurements techniques are required and/or a spatial accuracy of the velocity profiling is required This is due to factors related to hydraulic machinery down time, the often long time duration needed to explore the entire section area, the frequent cumbersome number of devices that needs to be handled simultaneously, or the impossibility to perform intrusive tests. In the case of non-intrusive flow profiling methods based on acoustic techniques, previous methods concentrated on using a large number of acoustic transducers placed around the measured section. Although feasible, this approach presents several major drawbacks such as a complicated signal timing, transmission, acquisition and recording system, resulting in a relative high cost of operation. In addition, because of the geometrical constraints, a desired number of sensors may not be installed. Recent results in acoustic flow metering based on wide band signals and adaptive beamforming proved that it is possible to achieve flow velocity profiles using less acoustic transducers. In a normal acoustic time of flight path the transducers are both emitters and receivers, sequentially changing their roles. In the new configuration, proposed in this paper, two new receivers are added on each side. Since the beam angles of each acoustic transducer are wide enough the newly added transducers can receive the transmitted signals and additional time of flight estimation can be done. Thus, several flow

Classification of change detection and change blindness from near-infrared spectroscopy signals

NASA Astrophysics Data System (ADS)

Tanaka, Hirokazu; Katura, Takusige

2011-08-01

Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.

Multiple signal classification algorithm for super-resolution fluorescence microscopy

PubMed Central

Agarwal, Krishna; Macháň, Radek

2016-01-01

Single-molecule localization techniques are restricted by long acquisition and computational times, or the need of special fluorophores or biologically toxic photochemical environments. Here we propose a statistical super-resolution technique of wide-field fluorescence microscopy we call the multiple signal classification algorithm which has several advantages. It provides resolution down to at least 50 nm, requires fewer frames and lower excitation power and works even at high fluorophore concentrations. Further, it works with any fluorophore that exhibits blinking on the timescale of the recording. The multiple signal classification algorithm shows comparable or better performance in comparison with single-molecule localization techniques and four contemporary statistical super-resolution methods for experiments of in vitro actin filaments and other independently acquired experimental data sets. We also demonstrate super-resolution at timescales of 245 ms (using 49 frames acquired at 200 frames per second) in samples of live-cell microtubules and live-cell actin filaments imaged without imaging buffers. PMID:27934858

Acoustic surface perception from naturally occurring step sounds of a dexterous hexapod robot

NASA Astrophysics Data System (ADS)

Cuneyitoglu Ozkul, Mine; Saranli, Afsar; Yazicioglu, Yigit

2013-10-01

Legged robots that exhibit dynamic dexterity naturally interact with the surface to generate complex acoustic signals carrying rich information on the surface as well as the robot platform itself. However, the nature of a legged robot, which is a complex, hybrid dynamic system, renders the more common approach of model-based system identification impractical. The present paper focuses on acoustic surface identification and proposes a non-model-based analysis and classification approach adopted from the speech processing literature. A novel feature set composed of spectral band energies augmented by their vector time derivatives and time-domain averaged zero crossing rate is proposed. Using a multi-dimensional vector classifier, these features carry enough information to accurately classify a range of commonly occurring indoor and outdoor surfaces without using of any mechanical system model. A comparative experimental study is carried out and classification performance and computational complexity are characterized. Different feature combinations, classifiers and changes in critical design parameters are investigated. A realistic and representative acoustic data set is collected with the robot moving at different speeds on a number of surfaces. The study demonstrates promising performance of this non-model-based approach, even in an acoustically uncontrolled environment. The approach also has good chance of performing in real-time.

An acoustic switch.

PubMed

Vanhille, Christian; Campos-Pozuelo, Cleofé

2014-01-01

The benefits derived from the development of acoustic transistors which act as switches or amplifiers have been reported in the literature. Here we propose a model of acoustic switch. We theoretically demonstrate that the device works: the input signal is totally restored at the output when the switch is on whereas the output signal nulls when the switch is off. The switch, on or off, depends on a secondary acoustic field capable to manipulate the main acoustic field. The model relies on the attenuation effect of many oscillating bubbles on the main travelling wave in the liquid, as well as on the capacity of the secondary acoustic wave to move the bubbles. This model evidences the concept of acoustic switch (transistor) with 100% efficiency. Copyright © 2013 Elsevier B.V. All rights reserved.

Acoustic signal detection of manatee calls

NASA Astrophysics Data System (ADS)

Niezrecki, Christopher; Phillips, Richard; Meyer, Michael; Beusse, Diedrich O.

2003-04-01

The West Indian manatee (trichechus manatus latirostris) has become endangered partly because of a growing number of collisions with boats. A system to warn boaters of the presence of manatees, that can signal to boaters that manatees are present in the immediate vicinity, could potentially reduce these boat collisions. In order to identify the presence of manatees, acoustic methods are employed. Within this paper, three different detection algorithms are used to detect the calls of the West Indian manatee. The detection systems are tested in the laboratory using simulated manatee vocalizations from an audio compact disc. The detection method that provides the best overall performance is able to correctly identify ~=96% of the manatee vocalizations. However the system also results in a false positive rate of ~=16%. The results of this work may ultimately lead to the development of a manatee warning system that can warn boaters of the presence of manatees.

Acoustic Remote Sensing

NASA Astrophysics Data System (ADS)

Dowling, David R.; Sabra, Karim G.

2015-01-01

Acoustic waves carry information about their source and collect information about their environment as they propagate. This article reviews how these information-carrying and -collecting features of acoustic waves that travel through fluids can be exploited for remote sensing. In nearly all cases, modern acoustic remote sensing involves array-recorded sounds and array signal processing to recover multidimensional results. The application realm for acoustic remote sensing spans an impressive range of signal frequencies (10-2 to 107 Hz) and distances (10-2 to 107 m) and involves biomedical ultrasound imaging, nondestructive evaluation, oil and gas exploration, military systems, and Nuclear Test Ban Treaty monitoring. In the past two decades, approaches have been developed to robustly localize remote sources; remove noise and multipath distortion from recorded signals; and determine the acoustic characteristics of the environment through which the sound waves have traveled, even when the recorded sounds originate from uncooperative sources or are merely ambient noise.

How the environment shapes animal signals: a test of the acoustic adaptation hypothesis in frogs.

PubMed

Goutte, S; Dubois, A; Howard, S D; Márquez, R; Rowley, J J L; Dehling, J M; Grandcolas, P; Xiong, R C; Legendre, F

2018-01-01

Long-distance acoustic signals are widely used in animal communication systems and, in many cases, are essential for reproduction. The acoustic adaptation hypothesis (AAH) implies that acoustic signals should be selected for further transmission and better content integrity under the acoustic constraints of the habitat in which they are produced. In this study, we test predictions derived from the AAH in frogs. Specifically, we focus on the difference between torrent frogs and frogs calling in less noisy habitats. Torrents produce sounds that can mask frog vocalizations and constitute a major acoustic constraint on call evolution. We combine data collected in the field, material from scientific collections and the literature for a total of 79 primarily Asian species, of the families Ranidae, Rhacophoridae, Dicroglossidae and Microhylidae. Using phylogenetic comparative methods and including morphological and environmental potential confounding factors, we investigate putatively adaptive call features in torrent frogs. We use broad habitat categories as well as fine-scale habitat measurements and test their correlation with six call characteristics. We find mixed support for the AAH. Spectral features of torrent frog calls are different from those of frogs calling in other habitats and are related to ambient noise levels, as predicted by the AAH. However, temporal call features do not seem to be shaped by the frogs' calling habitats. Our results underline both the complexity of call evolution and the need to consider multiple factors when investigating this issue. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

Magneto-acoustic imaging by continuous-wave excitation.

PubMed

Shunqi, Zhang; Zhou, Xiaoqing; Tao, Yin; Zhipeng, Liu

2017-04-01

The electrical characteristics of tissue yield valuable information for early diagnosis of pathological changes. Magneto-acoustic imaging is a functional approach for imaging of electrical conductivity. This study proposes a continuous-wave magneto-acoustic imaging method. A kHz-range continuous signal with an amplitude range of several volts is used to excite the magneto-acoustic signal and improve the signal-to-noise ratio. The magneto-acoustic signal amplitude and phase are measured to locate the acoustic source via lock-in technology. An optimisation algorithm incorporating nonlinear equations is used to reconstruct the magneto-acoustic source distribution based on the measured amplitude and phase at various frequencies. Validation simulations and experiments were performed in pork samples. The experimental and simulation results agreed well. While the excitation current was reduced to 10 mA, the acoustic signal magnitude increased up to 10 -7  Pa. Experimental reconstruction of the pork tissue showed that the image resolution reached mm levels when the excitation signal was in the kHz range. The signal-to-noise ratio of the detected magneto-acoustic signal was improved by more than 25 dB at 5 kHz when compared to classical 1 MHz pulse excitation. The results reported here will aid further research into magneto-acoustic generation mechanisms and internal tissue conductivity imaging.

Evidence for cultural dialects in vocal emotion expression: acoustic classification within and across five nations.

PubMed

Laukka, Petri; Neiberg, Daniel; Elfenbein, Hillary Anger

2014-06-01

The possibility of cultural differences in the fundamental acoustic patterns used to express emotion through the voice is an unanswered question central to the larger debate about the universality versus cultural specificity of emotion. This study used emotionally inflected standard-content speech segments expressing 11 emotions produced by 100 professional actors from 5 English-speaking cultures. Machine learning simulations were employed to classify expressions based on their acoustic features, using conditions where training and testing were conducted on stimuli coming from either the same or different cultures. A wide range of emotions were classified with above-chance accuracy in cross-cultural conditions, suggesting vocal expressions share important characteristics across cultures. However, classification showed an in-group advantage with higher accuracy in within- versus cross-cultural conditions. This finding demonstrates cultural differences in expressive vocal style, and supports the dialect theory of emotions according to which greater recognition of expressions from in-group members results from greater familiarity with culturally specific expressive styles.

Study of acoustic correlates associate with emotional speech

NASA Astrophysics Data System (ADS)

Yildirim, Serdar; Lee, Sungbok; Lee, Chul Min; Bulut, Murtaza; Busso, Carlos; Kazemzadeh, Ebrahim; Narayanan, Shrikanth

2004-10-01

This study investigates the acoustic characteristics of four different emotions expressed in speech. The aim is to obtain detailed acoustic knowledge on how a speech signal is modulated by changes from neutral to a certain emotional state. Such knowledge is necessary for automatic emotion recognition and classification and emotional speech synthesis. Speech data obtained from two semi-professional actresses are analyzed and compared. Each subject produces 211 sentences with four different emotions; neutral, sad, angry, happy. We analyze changes in temporal and acoustic parameters such as magnitude and variability of segmental duration, fundamental frequency and the first three formant frequencies as a function of emotion. Acoustic differences among the emotions are also explored with mutual information computation, multidimensional scaling and acoustic likelihood comparison with normal speech. Results indicate that speech associated with anger and happiness is characterized by longer duration, shorter interword silence, higher pitch and rms energy with wider ranges. Sadness is distinguished from other emotions by lower rms energy and longer interword silence. Interestingly, the difference in formant pattern between [happiness/anger] and [neutral/sadness] are better reflected in back vowels such as /a/(/father/) than in front vowels. Detailed results on intra- and interspeaker variability will be reported.

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning.

PubMed

Begum, Shahina; Barua, Shaibal; Ahmed, Mobyen Uddin

2014-07-03

Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

Artillery/mortar round type classification to increase system situational awareness

NASA Astrophysics Data System (ADS)

Desai, Sachi; Grasing, David; Morcos, Amir; Hohil, Myron

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Acoustic emission frequency discrimination

NASA Technical Reports Server (NTRS)

Sugg, Frank E. (Inventor); Graham, Lloyd J. (Inventor)

1988-01-01

In acoustic emission nondestructive testing, broadband frequency noise is distinguished from narrow banded acoustic emission signals, since the latter are valid events indicative of structural flaws in the material being examined. This is accomplished by separating out those signals which contain frequency components both within and beyond (either above or below) the range of valid acoustic emission events. Application to acoustic emission monitoring during nondestructive bond verification and proof loading of undensified tiles on the Space Shuttle Orbiter is considered.

Acoustic transducer for nuclear reactor monitoring

DOEpatents

Ahlgren, Frederic F.; Scott, Paul F.

1977-01-01

A transducer to monitor a parameter and produce an acoustic signal from which the monitored parameter can be recovered. The transducer comprises a modified Galton whistle which emits a narrow band acoustic signal having a frequency dependent upon the parameter being monitored, such as the temperature of the cooling media of a nuclear reactor. Multiple locations within a reactor are monitored simultaneously by a remote acoustic receiver by providing a plurality of transducers each designed so that the acoustic signal it emits has a frequency distinct from the frequencies of signals emitted by the other transducers, whereby each signal can be unambiguously related to a particular transducer.

Wake acoustic analysis and image decomposition via beamforming of microphone signal projections on wavelet subspaces

DOT National Transportation Integrated Search

2006-05-08

This paper describes the integration of wavelet analysis and time-domain beamforming : of microphone array output signals for analyzing the acoustic emissions from airplane : generated wake vortices. This integrated process provides visual and quanti...

Amplitude Modulations of Acoustic Communication Signals

NASA Astrophysics Data System (ADS)

Turesson, Hjalmar K.

2011-12-01

In human speech, amplitude modulations at 3 -- 8 Hz are important for discrimination and detection. Two different neurophysiological theories have been proposed to explain this effect. The first theory proposes that, as a consequence of neocortical synaptic dynamics, signals that are amplitude modulated at 3 -- 8 Hz are propagated better than un-modulated signals, or signals modulated above 8 Hz. This suggests that neural activity elicited by vocalizations modulated at 3 -- 8 Hz is optimally transmitted, and the vocalizations better discriminated and detected. The second theory proposes that 3 -- 8 Hz amplitude modulations interact with spontaneous neocortical oscillations. Specifically, vocalizations modulated at 3 -- 8 Hz entrain local populations of neurons, which in turn, modulate the amplitude of high frequency gamma oscillations. This suggests that vocalizations modulated at 3 -- 8 Hz should induce stronger cross-frequency coupling. Similar to human speech, we found that macaque monkey vocalizations also are amplitude modulated between 3 and 8 Hz. Humans and macaque monkeys share similarities in vocal production, implying that the auditory systems subserving perception of acoustic communication signals also share similarities. Based on the similarities between human speech and macaque monkey vocalizations, we addressed how amplitude modulated vocalizations are processed in the auditory cortex of macaque monkeys, and what behavioral relevance modulations may have. Recording single neuron activity, as well as, the activity of local populations of neurons allowed us to test both of the neurophysiological theories presented above. We found that single neuron responses to vocalizations amplitude modulated at 3 -- 8 Hz resulted in better stimulus discrimination than vocalizations lacking 3 -- 8 Hz modulations, and that the effect most likely was mediated by synaptic dynamics. In contrast, we failed to find support for the oscillation-based model proposing a

Acoustic gravity microseismic pressure signal at shallow stations

NASA Astrophysics Data System (ADS)

Peureux, Charles; Ardhuin, Fabrice; Royer, Jean-Yves

2017-04-01

It has been known for decades that the background permanent seismic noise, the so-called microseimic signal, is generated by the nonlinear interaction of oppositely travelling ocean surface waves [Longuet-Higgins 1951]. It can especially be used to infer the time variability of short ocean waves statistics [Peureux and Ardhuin 2016]. However, better quantitative estimates of the latter are made difficult due to a poor knowledge of the Earth's crust characteristics, whose coupling with acoustic modes can affect large uncertainties to the frequency response at the bottom of the ocean. The pressure field at depths less than an acoustic wave length to the surface is made of evanescent acoustic-gravity modes [Cox and Jacobs 1989]. For this reason, they are less affected by the ocean bottom composition. This near field is recorded and analyzed in the frequency range 0.1 to 0.5 Hz approximately, at two locations : at a shallow site in the North-East Atlantic continental shelf and a deep water site in the Southern Indian ocean, at the ocean bottom and 100 m below sea-surface and in the upper part of the water column respectively. Evanescent and propagating Rayleigh modes are compared against theoretical predictions. Comparisons against surface waves hindcast based on WAVEWATCH(R) III modelling framework help assessing its performances and can be used to help future model improvements. References Longuet-Higgins, M. S., A Theory of the Origin of Microseisms, Philos. Trans. Royal Soc. A, The Royal Society, 1950, 243, 1-3. Peureux, C. and Ardhuin, F., Ocean bottom pressure records from the Cascadia array and short surface gravity waves, J. Geophys. Res. Oceans, 2016, 121, 2862-2873. Cox, C. S. & Jacobs, D. C., Cartesian diver observations of double frequency pressure fluctuations in the upper levels of the ocean, Geophys. Res. Lett., 1989, 16, 807-810.

Acoustic imaging system

DOEpatents

Smith, Richard W.

1979-01-01

An acoustic imaging system for displaying an object viewed by a moving array of transducers as the array is pivoted about a fixed point within a given plane. A plurality of transducers are fixedly positioned and equally spaced within a laterally extending array and operatively directed to transmit and receive acoustic signals along substantially parallel transmission paths. The transducers are sequentially activated along the array to transmit and receive acoustic signals according to a preestablished sequence. Means are provided for generating output voltages for each reception of an acoustic signal, corresponding to the coordinate position of the object viewed as the array is pivoted. Receptions from each of the transducers are presented on the same display at coordinates corresponding to the actual position of the object viewed to form a plane view of the object scanned.

Acoustic Location of Lightning Using Interferometric Techniques

NASA Astrophysics Data System (ADS)

Erives, H.; Arechiga, R. O.; Stock, M.; Lapierre, J. L.; Edens, H. E.; Stringer, A.; Rison, W.; Thomas, R. J.

2013-12-01

Acoustic arrays have been used to accurately locate thunder sources in lightning flashes. The acoustic arrays located around the Magdalena mountains of central New Mexico produce locations which compare quite well with source locations provided by the New Mexico Tech Lightning Mapping Array. These arrays utilize 3 outer microphones surrounding a 4th microphone located at the center, The location is computed by band-passing the signal to remove noise, and then computing the cross correlating the outer 3 microphones with respect the center reference microphone. While this method works very well, it works best on signals with high signal to noise ratios; weaker signals are not as well located. Therefore, methods are being explored to improve the location accuracy and detection efficiency of the acoustic location systems. The signal received by acoustic arrays is strikingly similar to th signal received by radio frequency interferometers. Both acoustic location systems and radio frequency interferometers make coherent measurements of a signal arriving at a number of closely spaced antennas. And both acoustic and interferometric systems then correlate these signals between pairs of receivers to determine the direction to the source of the received signal. The primary difference between the two systems is the velocity of propagation of the emission, which is much slower for sound. Therefore, the same frequency based techniques that have been used quite successfully with radio interferometers should be applicable to acoustic based measurements as well. The results presented here are comparisons between the location results obtained with current cross correlation method and techniques developed for radio frequency interferometers applied to acoustic signals. The data were obtained during the summer 2013 storm season using multiple arrays sensitive to both infrasonic frequency and audio frequency acoustic emissions from lightning. Preliminary results show that

Micro acoustic spectrum analyzer

DOEpatents

Schubert, W. Kent; Butler, Michael A.; Adkins, Douglas R.; Anderson, Larry F.

2004-11-23

A micro acoustic spectrum analyzer for determining the frequency components of a fluctuating sound signal comprises a microphone to pick up the fluctuating sound signal and produce an alternating current electrical signal; at least one microfabricated resonator, each resonator having a different resonant frequency, that vibrate in response to the alternating current electrical signal; and at least one detector to detect the vibration of the microfabricated resonators. The micro acoustic spectrum analyzer can further comprise a mixer to mix a reference signal with the alternating current electrical signal from the microphone to shift the frequency spectrum to a frequency range that is a better matched to the resonant frequencies of the microfabricated resonators. The micro acoustic spectrum analyzer can be designed specifically for portability, size, cost, accuracy, speed, power requirements, and use in a harsh environment. The micro acoustic spectrum analyzer is particularly suited for applications where size, accessibility, and power requirements are limited, such as the monitoring of industrial equipment and processes, detection of security intrusions, or evaluation of military threats.

Techniques of EMG signal analysis: detection, processing, classification and applications

PubMed Central

Hussain, M.S.; Mohd-Yasin, F.

2006-01-01

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications. PMID:16799694

Listening to the Deep: live monitoring of ocean noise and cetacean acoustic signals.

PubMed

André, M; van der Schaar, M; Zaugg, S; Houégnigan, L; Sánchez, A M; Castell, J V

2011-01-01

The development and broad use of passive acoustic monitoring techniques have the potential to help assessing the large-scale influence of artificial noise on marine organisms and ecosystems. Deep-sea observatories have the potential to play a key role in understanding these recent acoustic changes. LIDO (Listening to the Deep Ocean Environment) is an international project that is allowing the real-time long-term monitoring of marine ambient noise as well as marine mammal sounds at cabled and standalone observatories. Here, we present the overall development of the project and the use of passive acoustic monitoring (PAM) techniques to provide the scientific community with real-time data at large spatial and temporal scales. Special attention is given to the extraction and identification of high frequency cetacean echolocation signals given the relevance of detecting target species, e.g. beaked whales, in mitigation processes, e.g. during military exercises. Copyright © 2011. Published by Elsevier Ltd.

Acoustic emission from single point machining: Part 2, Signal changes with tool wear. Revised

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.

1989-12-31

Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

pySPACE-a signal processing and classification environment in Python.

PubMed

Krell, Mario M; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H; Kirchner, Elsa A; Kirchner, Frank

2013-01-01

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

Implementation and performance evaluation of acoustic denoising algorithms for UAV

NASA Astrophysics Data System (ADS)

Chowdhury, Ahmed Sony Kamal

Unmanned Aerial Vehicles (UAVs) have become popular alternative for wildlife monitoring and border surveillance applications. Elimination of the UAV's background noise and classifying the target audio signal effectively are still a major challenge. The main goal of this thesis is to remove UAV's background noise by means of acoustic denoising techniques. Existing denoising algorithms, such as Adaptive Least Mean Square (LMS), Wavelet Denoising, Time-Frequency Block Thresholding, and Wiener Filter, were implemented and their performance evaluated. The denoising algorithms were evaluated for average Signal to Noise Ratio (SNR), Segmental SNR (SSNR), Log Likelihood Ratio (LLR), and Log Spectral Distance (LSD) metrics. To evaluate the effectiveness of the denoising algorithms on classification of target audio, we implemented Support Vector Machine (SVM) and Naive Bayes classification algorithms. Simulation results demonstrate that LMS and Discrete Wavelet Transform (DWT) denoising algorithm offered superior performance than other algorithms. Finally, we implemented the LMS and DWT algorithms on a DSP board for hardware evaluation. Experimental results showed that LMS algorithm's performance is robust compared to DWT for various noise types to classify target audio signals.

Surface acoustic wave dust deposition monitor

DOEpatents

Fasching, G.E.; Smith, N.S. Jr.

1988-02-12

A system is disclosed for using the attenuation of surface acoustic waves to monitor real time dust deposition rates on surfaces. The system includes a signal generator, a tone-burst generator/amplifier connected to a transmitting transducer for converting electrical signals into acoustic waves. These waves are transmitted through a path defining means adjacent to a layer of dust and then, in turn, transmitted to a receiving transducer for changing the attenuated acoustic wave to electrical signals. The signals representing the attenuated acoustic waves may be amplified and used in a means for analyzing the output signals to produce an output indicative of the dust deposition rates and/or values of dust in the layer. 8 figs.

Acoustic calibration apparatus for calibrating plethysmographic acoustic pressure sensors

NASA Technical Reports Server (NTRS)

Zuckerwar, Allan J. (Inventor); Davis, David C. (Inventor)

1995-01-01

An apparatus for calibrating an acoustic sensor is described. The apparatus includes a transmission material having an acoustic impedance approximately matching the acoustic impedance of the actual acoustic medium existing when the acoustic sensor is applied in actual in-service conditions. An elastic container holds the transmission material. A first sensor is coupled to the container at a first location on the container and a second sensor coupled to the container at a second location on the container, the second location being different from the first location. A sound producing device is coupled to the container and transmits acoustic signals inside the container.

Acoustic calibration apparatus for calibrating plethysmographic acoustic pressure sensors

NASA Technical Reports Server (NTRS)

Zuckerwar, Allan J. (Inventor); Davis, David C. (Inventor)

1994-01-01

An apparatus for calibrating an acoustic sensor is described. The apparatus includes a transmission material having an acoustic impedance approximately matching the acoustic impedance of the actual acoustic medium existing when the acoustic sensor is applied in actual in-service conditions. An elastic container holds the transmission material. A first sensor is coupled to the container at a first location on the container and a second sensor coupled to the container at a second location on the container, the second location being different from the first location. A sound producing device is coupled to the container and transmits acoustic signals inside the container.

Neural network classification of myoelectric signal for prosthesis control.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1991-12-01

An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions. Copyright © 1991. Published by Elsevier Ltd.

Introduction to acoustic emission

NASA Technical Reports Server (NTRS)

Possa, G.

1983-01-01

Typical acoustic emission signal characteristics are described and techniques which localize the signal source by processing the acoustic delay data from multiple sensors are discussed. The instrumentation, which includes sensors, amplifiers, pulse counters, a minicomputer and output devices is examined. Applications are reviewed.

A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions

DOE PAGES

Vann, Jason Michael; Karnowski, Thomas P.; Kerekes, Ryan; ...

2017-04-24

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projectionsmore » minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.« less

A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vann, Jason Michael; Karnowski, Thomas P.; Kerekes, Ryan

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projectionsmore » minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.« less

Pulse analysis of acoustic emission signals

NASA Technical Reports Server (NTRS)

Houghton, J. R.; Packman, P. F.

1977-01-01

A method for the signature analysis of pulses in the frequency domain and the time domain is presented. Fourier spectrum, Fourier transfer function, shock spectrum and shock spectrum ratio were examined in the frequency domain analysis and pulse shape deconvolution was developed for use in the time domain analysis. Comparisons of the relative performance of each analysis technique are made for the characterization of acoustic emission pulses recorded by a measuring system. To demonstrate the relative sensitivity of each of the methods to small changes in the pulse shape, signatures of computer modeled systems with analytical pulses are presented. Optimization techniques are developed and used to indicate the best design parameter values for deconvolution of the pulse shape. Several experiments are presented that test the pulse signature analysis methods on different acoustic emission sources. These include acoustic emission associated with (a) crack propagation, (b) ball dropping on a plate, (c) spark discharge, and (d) defective and good ball bearings. Deconvolution of the first few micro-seconds of the pulse train is shown to be the region in which the significant signatures of the acoustic emission event are to be found.

Pulse analysis of acoustic emission signals

NASA Technical Reports Server (NTRS)

Houghton, J. R.; Packman, P. F.

1977-01-01

A method for the signature analysis of pulses in the frequency domain and the time domain is presented. Fourier spectrum, Fourier transfer function, shock spectrum and shock spectrum ratio were examined in the frequency domain analysis, and pulse shape deconvolution was developed for use in the time domain analysis. Comparisons of the relative performance of each analysis technique are made for the characterization of acoustic emission pulses recorded by a measuring system. To demonstrate the relative sensitivity of each of the methods to small changes in the pulse shape, signatures of computer modeled systems with analytical pulses are presented. Optimization techniques are developed and used to indicate the best design parameters values for deconvolution of the pulse shape. Several experiments are presented that test the pulse signature analysis methods on different acoustic emission sources. These include acoustic emissions associated with: (1) crack propagation, (2) ball dropping on a plate, (3) spark discharge and (4) defective and good ball bearings. Deconvolution of the first few micro-seconds of the pulse train are shown to be the region in which the significant signatures of the acoustic emission event are to be found.

Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals

NASA Astrophysics Data System (ADS)

Griffin, James M.; Diaz, Fernanda; Geerling, Edgar; Clasing, Matias; Ponce, Vicente; Taylor, Chris; Turner, Sam; Michael, Ernest A.; Patricio Mena, F.; Bronfman, Leonardo

2017-02-01

By using acoustic emission (AE) it is possible to control deviations and surface quality during micro milling operations. The method of micro milling is used to manufacture a submillimetre waveguide where micro machining is employed to achieve the required superior finish and geometrical tolerances. Submillimetre waveguide technology is used in deep space signal retrieval where highest detection efficiencies are needed and therefore every possible signal loss in the receiver has to be avoided and stringent tolerances achieved. With a sub-standard surface finish the signals travelling along the waveguides dissipate away faster than with perfect surfaces where the residual roughness becomes comparable with the electromagnetic skin depth. Therefore, the higher the radio frequency the more critical this becomes. The method of time-frequency analysis (STFT) is used to transfer raw AE into more meaningful salient signal features (SF). This information was then correlated against the measured geometrical deviations and, the onset of catastrophic tool wear. Such deviations can be offset from different AE signals (different deviations from subsequent tests) and feedback for a final spring cut ensuring the geometrical accuracies are met. Geometrical differences can impact on the required transfer of AE signals (change in cut off frequencies and diminished SNR at the interface) and therefore errors have to be minimised to within 1 μm. Rules based on both Classification and Regression Trees (CART) and Neural Networks (NN) were used to implement a simulation displaying how such a control regime could be used as a real time controller, be it corrective measures (via spring cuts) over several initial machining passes or, with a micron cut introducing a level plain measure for allowing setup corrective measures (similar to a spirit level).

Predicting couple therapy outcomes based on speech acoustic features

PubMed Central

Nasir, Md; Baucom, Brian Robert; Narayanan, Shrikanth

2017-01-01

Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes. The therapy outcome prediction task in this work includes detecting whether there was a relationship improvement or not (posed as a binary classification) as well as discerning varying levels of improvement or decline in the relationship status (posed as a multiclass recognition task). We use each interlocutor’s acoustic speech signal characteristics such as vocal intonation and intensity, both independently and in relation to one another, as cues for predicting the therapy outcome. We also compare prediction performance with one obtained via standardized behavioral codes characterizing the relationship dynamics provided by human experts as features for automated classification. Our experiments, using data from a longitudinal clinical study of couples in distressed relations, showed that predictions of relationship outcomes obtained directly from vocal acoustics are comparable or superior to those obtained using human-rated behavioral codes as prediction features. In addition, combining direct signal-derived features with manually coded behavioral features improved the prediction performance in most cases, indicating the complementarity of relevant information captured by humans and machine algorithms. Additionally, considering the vocal properties of the interlocutors in relation to one another, rather than in isolation, showed to be important for improving the automatic prediction. This finding supports the notion that behavioral

Quantified acoustic-optical speech signal incongruity identifies cortical sites of audiovisual speech processing

PubMed Central

Bernstein, Lynne E.; Lu, Zhong-Lin; Jiang, Jintao

2008-01-01

A fundamental question about human perception is how the speech perceiving brain combines auditory and visual phonetic stimulus information. We assumed that perceivers learn the normal relationship between acoustic and optical signals. We hypothesized that when the normal relationship is perturbed by mismatching the acoustic and optical signals, cortical areas responsible for audiovisual stimulus integration respond as a function of the magnitude of the mismatch. To test this hypothesis, in a previous study, we developed quantitative measures of acoustic-optical speech stimulus incongruity that correlate with perceptual measures. In the current study, we presented low incongruity (LI, matched), medium incongruity (MI, moderately mismatched), and high incongruity (HI, highly mismatched) audiovisual nonsense syllable stimuli during fMRI scanning. Perceptual responses differed as a function of the incongruity level, and BOLD measures were found to vary regionally and quantitatively with perceptual and quantitative incongruity levels. Each increase in level of incongruity resulted in an increase in overall levels of cortical activity and in additional activations. However, the only cortical region that demonstrated differential sensitivity to the three stimulus incongruity levels (HI > MI > LI) was a subarea of the left supramarginal gyrus (SMG). The left SMG might support a fine-grained analysis of the relationship between audiovisual phonetic input in comparison with stored knowledge, as hypothesized here. The methods here show that quantitative manipulation of stimulus incongruity is a new and powerful tool for disclosing the system that processes audiovisual speech stimuli. PMID:18495091

Bio-inspired UAV routing, source localization, and acoustic signature classification for persistent surveillance

NASA Astrophysics Data System (ADS)

Burman, Jerry; Hespanha, Joao; Madhow, Upamanyu; Pham, Tien

2011-06-01

A team consisting of Teledyne Scientific Company, the University of California at Santa Barbara and the Army Research Laboratory* is developing technologies in support of automated data exfiltration from heterogeneous battlefield sensor networks to enhance situational awareness for dismounts and command echelons. Unmanned aerial vehicles (UAV) provide an effective means to autonomously collect data from a sparse network of unattended ground sensors (UGSs) that cannot communicate with each other. UAVs are used to reduce the system reaction time by generating autonomous collection routes that are data-driven. Bio-inspired techniques for search provide a novel strategy to detect, capture and fuse data. A fast and accurate method has been developed to localize an event by fusing data from a sparse number of UGSs. This technique uses a bio-inspired algorithm based on chemotaxis or the motion of bacteria seeking nutrients in their environment. A unique acoustic event classification algorithm was also developed based on using swarm optimization. Additional studies addressed the problem of routing multiple UAVs, optimally placing sensors in the field and locating the source of gunfire at helicopters. A field test was conducted in November of 2009 at Camp Roberts, CA. The field test results showed that a system controlled by bio-inspired software algorithms can autonomously detect and locate the source of an acoustic event with very high accuracy and visually verify the event. In nine independent test runs of a UAV, the system autonomously located the position of an explosion nine times with an average accuracy of 3 meters. The time required to perform source localization using the UAV was on the order of a few minutes based on UAV flight times. In June 2011, additional field tests of the system will be performed and will include multiple acoustic events, optimal sensor placement based on acoustic phenomenology and the use of the International Technology Alliance (ITA

A new feature extraction method for signal classification applied to cord dorsum potentials detection

PubMed Central

Vidaurre, D.; Rodríguez, E. E.; Bielza, C.; Larrañaga, P.; Rudomin, P.

2012-01-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods. PMID:22929924

A new feature extraction method for signal classification applied to cord dorsum potential detection.

PubMed

Vidaurre, D; Rodríguez, E E; Bielza, C; Larrañaga, P; Rudomin, P

2012-10-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

PubMed

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

PubMed Central

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-01-01

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine. PMID:26506347

Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

NASA Astrophysics Data System (ADS)

Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y.; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

2016-09-01

We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.

Automated Method of Frequency Determination in Software Metric Data Through the Use of the Multiple Signal Classification (MUSIC) Algorithm

DTIC Science & Technology

1998-06-26

METHOD OF FREQUENCY DETERMINATION 4 IN SOFTWARE METRIC DATA THROUGH THE USE OF THE 5 MULTIPLE SIGNAL CLASSIFICATION ( MUSIC ) ALGORITHM 6 7 STATEMENT OF...graph showing the estimated power spectral 12 density (PSD) generated by the multiple signal classification 13 ( MUSIC ) algorithm from the data set used...implemented in this module; however, it is preferred to use 1 the Multiple Signal Classification ( MUSIC ) algorithm. The MUSIC 2 algorithm is

Some Dimensions of Auditory Sonar Signal Perception and Their Relationships to Target Classification

DTIC Science & Technology

1981-02-13

a priori how the sample of experimental stimuli related to the classification stereotypes of experienced sonar personnel, Question 6 was addressed by...projections on some of the experimentally identified dimensions are associ- ated with a high degree of classification success, but signals that lack ,strong...11 Hypotheses ......................... 11 Procedure ....... .. .. ......................... 11 Experimental Stimuli

Phylogenetic signal in the acoustic parameters of the advertisement calls of four clades of anurans.

PubMed

Gingras, Bruno; Mohandesan, Elmira; Boko, Drasko; Fitch, W Tecumseh

2013-07-01

Anuran vocalizations, especially their advertisement calls, are largely species-specific and can be used to identify taxonomic affiliations. Because anurans are not vocal learners, their vocalizations are generally assumed to have a strong genetic component. This suggests that the degree of similarity between advertisement calls may be related to large-scale phylogenetic relationships. To test this hypothesis, advertisement calls from 90 species belonging to four large clades (Bufo, Hylinae, Leptodactylus, and Rana) were analyzed. Phylogenetic distances were estimated based on the DNA sequences of the 12S mitochondrial ribosomal RNA gene, and, for a subset of 49 species, on the rhodopsin gene. Mean values for five acoustic parameters (coefficient of variation of root-mean-square amplitude, dominant frequency, spectral flux, spectral irregularity, and spectral flatness) were computed for each species. We then tested for phylogenetic signal on the body-size-corrected residuals of these five parameters, using three statistical tests (Moran's I, Mantel, and Blomberg's K) and three models of genetic distance (pairwise distances, Abouheif's proximities, and the variance-covariance matrix derived from the phylogenetic tree). A significant phylogenetic signal was detected for most acoustic parameters on the 12S dataset, across statistical tests and genetic distance models, both for the entire sample of 90 species and within clades in several cases. A further analysis on a subset of 49 species using genetic distances derived from rhodopsin and from 12S broadly confirmed the results obtained on the larger sample, indicating that the phylogenetic signals observed in these acoustic parameters can be detected using a variety of genetic distance models derived either from a variable mitochondrial sequence or from a conserved nuclear gene. We found a robust relationship, in a large number of species, between anuran phylogenetic relatedness and acoustic similarity in the

Low frequency acoustic microscope

DOEpatents

Khuri-Yakub, Butrus T.

1986-11-04

A scanning acoustic microscope is disclosed for the detection and location of near surface flaws, inclusions or voids in a solid sample material. A focused beam of acoustic energy is directed at the sample with its focal plane at the subsurface flaw, inclusion or void location. The sample is scanned with the beam. Detected acoustic energy specularly reflected and mode converted at the surface of the sample and acoustic energy reflected by subsurface flaws, inclusions or voids at the focal plane are used for generating an interference signal which is processed and forms a signal indicative of the subsurface flaws, inclusions or voids.

pySPACE—a signal processing and classification environment in Python

PubMed Central

Krell, Mario M.; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H.; Kirchner, Elsa A.; Kirchner, Frank

2013-01-01

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries. PMID:24399965

Automated aural classification used for inter-species discrimination of cetaceans.

PubMed

Binder, Carolyn M; Hines, Paul C

2014-04-01

Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

Neural Mechanisms for Acoustic Signal Detection under Strong Masking in an Insect

PubMed Central

Römer, Heiner

2015-01-01

Communication is fundamental for our understanding of behavior. In the acoustic modality, natural scenes for communication in humans and animals are often very noisy, decreasing the chances for signal detection and discrimination. We investigated the mechanisms enabling selective hearing under natural noisy conditions for auditory receptors and interneurons of an insect. In the studied katydid Mecopoda elongata species-specific calling songs (chirps) are strongly masked by signals of another species, both communicating in sympatry. The spectral properties of the two signals are similar and differ only in a small frequency band at 2 kHz present in the chirping species. Receptors sharply tuned to 2 kHz are completely unaffected by the masking signal of the other species, whereas receptors tuned to higher audio and ultrasonic frequencies show complete masking. Intracellular recordings of identified interneurons revealed two mechanisms providing response selectivity to the chirp. (1) Response selectivity is when several identified interneurons exhibit remarkably selective responses to the chirps, even at signal-to-noise ratios of −21 dB, since they are sharply tuned to 2 kHz. Their dendritic arborizations indicate selective connectivity with low-frequency receptors tuned to 2 kHz. (2) Novelty detection is when a second group of interneurons is broadly tuned but, because of strong stimulus-specific adaptation to the masker spectrum and “novelty detection” to the 2 kHz band present only in the conspecific signal, these interneurons start to respond selectively to the chirp shortly after the onset of the continuous masker. Both mechanisms provide the sensory basis for hearing at unfavorable signal-to-noise ratios. SIGNIFICANCE STATEMENT Animal and human acoustic communication may suffer from the same “cocktail party problem,” when communication happens in noisy social groups. We address solutions for this problem in a model system of two katydids, where one

Acoustic effects of the ATOC signal (75 Hz, 195 dB) on dolphins and whales.

PubMed

Au, W W; Nachtigall, P E; Pawloski, J L

1997-05-01

The Acoustic Thermometry of Ocean Climate (ATOC) program of Scripps Institution of Oceanography and the Applied Physics Laboratory, University of Washington, will broadcast a low-frequency 75-Hz phase modulated acoustic signal over ocean basins in order to study ocean temperatures on a global scale and examine the effects of global warming. One of the major concerns is the possible effect of the ATOC signal on marine life, especially on dolphins and whales. In order to address this issue, the hearing sensitivity of a false killer whale (Pseudorca crassidens) and a Risso's dolphin (Grampus griseus) to the ATOC sound was measured behaviorally. A staircase procedure with the signal levels being changed in 1-dB steps was used to measure the animals' threshold to the actual ATOC coded signal. The results indicate that small odontocetes such as the Pseudorca and Grampus swimming directly above the ATOC source will not hear the signal unless they dive to a depth of approximately 400 m. A sound propagation analysis suggests that the sound-pressure level at ranges greater than 0.5 km will be less than 130 dB for depths down to about 500 m. Several species of baleen whales produce sounds much greater than 170-180 dB. With the ATOC source on the axis of the deep sound channel (greater than 800 m), the ATOC signal will probably have minimal physical and physiological effects on cetaceans.

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

NASA Astrophysics Data System (ADS)

Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

2017-01-01

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Acoustic Signal Characteristics Measured with the LAMBDA III During CHURCH STROKE III

DTIC Science & Technology

1980-09-15

analysis. Dr. William M. Carey and Dr. Richard Doolittle participated in various stages of acquisition, processing and analysis of the information...reported herein. Drs. Carey , Doolittle and Mr. Gereben are the authors of this report. (U) This report: Acoustic Signal Characteristics Measured with... Tortugas Terrace and the East Yucatan Channel,the Catoche Tongue and the Eastern region of the Gulf of Mexico. (U) The exercise was conducted by the Long

Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

NASA Astrophysics Data System (ADS)

Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

2015-08-01

Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, Ada

Demodulation of acoustic telemetry binary phase shift keying signal based on high-order Duffing system

NASA Astrophysics Data System (ADS)

Yan, Bing-Nan; Liu, Chong-Xin; Ni, Jun-Kang; Zhao, Liang

2016-10-01

In order to grasp the downhole situation immediately, logging while drilling (LWD) technology is adopted. One of the LWD technologies, called acoustic telemetry, can be successfully applied to modern drilling. It is critical for acoustic telemetry technology that the signal is successfully transmitted to the ground. In this paper, binary phase shift keying (BPSK) is used to modulate carrier waves for the transmission and a new BPSK demodulation scheme based on Duffing chaos is investigated. Firstly, a high-order system is given in order to enhance the signal detection capability and it is realized through building a virtual circuit using an electronic workbench (EWB). Secondly, a new BPSK demodulation scheme is proposed based on the intermittent chaos phenomena of the new Duffing system. Finally, a system variable crossing zero-point equidistance method is proposed to obtain the phase difference between the system and the BPSK signal. Then it is determined that the digital signal transmitted from the bottom of the well is ‘0’ or ‘1’. The simulation results show that the demodulation method is feasible. Project supported by the National Natural Science Foundation of China (Grant No. 51177117) and the National Key Science & Technology Special Projects, China (Grant No. 2011ZX05021-005).

Enhancing acoustic signal response and absorption of an underwater coated plate by embedding periodical inhomogeneities.

PubMed

Zhang, Yanni; Pan, Jie

2017-12-01

An underwater structure is proposed for simultaneous detection and stealth purposes by embedding periodic signal conditioning plates (SCPs) at the interface of two elastic coatings attached to an elastic plate. Results show that the embedded SCPs can enhance sound absorption at frequencies below the coincidence frequency of the plate (f c ). Significantly enhanced absorption occurs at five peaks, of which the peak due to excited localized bending resonance in the outer coating between SCPs is the most significant. When the dilatational velocity of the outer coating equals that of the inner coating, nearly total absorption occurs in a wideband, owing to strong coupling between the localized waveguide resonance in the outer coating and that in the inner coating, and the diffraction waves by the SCPs. Meanwhile, an amplified acoustic signal of over 14 dB is observed at most frequencies within 0 ∼ f c at the coatings' interface close to the SCPs' edges, owing to focused stress formed there. Peaks in the signal response at maximal 30 dB are also observed. These peak frequencies are coincident with or close to the peak frequencies of absorption, demonstrating that significantly enhanced acoustic signal and absorption can be achieved simultaneously through the use of embedded periodic SCPs.

Intelligent feature selection techniques for pattern classification of Lamb wave signals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

Discrimination of acoustic communication signals by grasshoppers (Chorthippus biguttulus): temporal resolution, temporal integration, and the impact of intrinsic noise.

PubMed

Ronacher, Bernhard; Wohlgemuth, Sandra; Vogel, Astrid; Krahe, Rüdiger

2008-08-01

A characteristic feature of hearing systems is their ability to resolve both fast and subtle amplitude modulations of acoustic signals. This applies also to grasshoppers, which for mate identification rely mainly on the characteristic temporal patterns of their communication signals. Usually the signals arriving at a receiver are contaminated by various kinds of noise. In addition to extrinsic noise, intrinsic noise caused by stochastic processes within the nervous system contributes to making signal recognition a difficult task. The authors asked to what degree intrinsic noise affects temporal resolution and, particularly, the discrimination of similar acoustic signals. This study aims at exploring the neuronal basis for sexual selection, which depends on exploiting subtle differences between basically similar signals. Applying a metric, by which the similarities of spike trains can be assessed, the authors investigated how well the communication signals of different individuals of the same species could be discriminated and correctly classified based on the responses of auditory neurons. This spike train metric yields clues to the optimal temporal resolution with which spike trains should be evaluated. (c) 2008 APA, all rights reserved

Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

NASA Astrophysics Data System (ADS)

Chestek, Cynthia A.; Gilja, Vikash; Blabe, Christine H.; Foster, Brett L.; Shenoy, Krishna V.; Parvizi, Josef; Henderson, Jaimie M.

2013-04-01

Objective. Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.Approach. We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants. Main results. Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance. These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training

Classifiers utilized to enhance acoustic based sensors to identify round types of artillery/mortar

NASA Astrophysics Data System (ADS)

Grasing, David; Desai, Sachi; Morcos, Amir

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Use of Seafloor Electromagnetic and Acoustic Backscatter Data for Seafloor Classification: An Example From Martha's Vineyard, Massachusetts.

NASA Astrophysics Data System (ADS)

Evans, R. L.; Kraft, B.; Mayer, L.

2006-12-01

Near surface offshore geophysical data allow sediment classification in coastal settings at high levels of spatial detail. We present data from offshore Martha's Vineyard, Massachusetts collected as part of the Office of Naval Researchś Mine Burial Prediction program. Seafloor electromagnetic data provide estimates of near surface porosity at approximately 10m intervals along each tow-line. In addition, the area has undergone repeat surveys with high resolution acoustic backscatter and bathymetry. In some locations, the geophysical data has been groundtruthed by grab sampling and coring. We examine the spatial variability in near surface sediment properties on the basis of the geophysical data. The EM data are particularly well suited to constructing semi-variograms to display length scales of variability. Preliminary examination does not show any obvious correlation between the EM data and acoustic backscatter, however, further processing of the backscatter is being carried out and so this result is tentative.

Feature Extraction for Track Section Status Classification Based on UGW Signals

PubMed Central

Yang, Yuan; Shi, Lin

2018-01-01

Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW). This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works. PMID:29673156

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

PubMed Central

Morison, Gordon; Boreham, Philip

2018-01-01

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring. PMID:29385030

Voice based gender classification using machine learning

NASA Astrophysics Data System (ADS)

Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

2017-11-01

Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

Seismo-acoustic signals associated with degassing explosions recorded at Shishaldin Volcano, Alaska, 2003-2004

USGS Publications Warehouse

Petersen, T.

2007-01-01

In summer 2003, a Chaparral Model 2 microphone was deployed at Shishaldin Volcano, Aleutian Islands, Alaska. The pressure sensor was co-located with a short-period seismometer on the volcano’s north flank at a distance of 6.62 km from the active summit vent. The seismo-acoustic data exhibit a correlation between impulsive acoustic signals (1–2 Pa) and long-period (LP, 1–2 Hz) earthquakes. Since it last erupted in 1999, Shishaldin has been characterized by sustained seismicity consisting of many hundreds to two thousand LP events per day. The activity is accompanied by up to ∼200 m high discrete gas puffs exiting the small summit vent, but no significant eruptive activity has been confirmed. The acoustic waveforms possess similarity throughout the data set (July 2003–November 2004) indicating a repetitive source mechanism. The simplicity of the acoustic waveforms, the impulsive onsets with relatively short (∼10–20 s) gradually decaying codas and the waveform similarities suggest that the acoustic pulses are generated at the fluid–air interface within an open-vent system. SO2 measurements have revealed a low SO2 flux, suggesting a hydrothermal system with magmatic gases leaking through. This hypothesis is supported by the steady-state nature of Shishaldin’s volcanic system since 1999. Time delays between the seismic LP and infrasound onsets were acquired from a representative day of seismo-acoustic data. A simple model was used to estimate source depths. The short seismo-acoustic delay times have revealed that the seismic and acoustic sources are co-located at a depth of 240±200 m below the crater rim. This shallow depth is confirmed by resonance of the upper portion of the open conduit, which produces standing waves with f=0.3 Hz in the acoustic waveform codas. The infrasound data has allowed us to relate Shishaldin’s LP earthquakes to degassing explosions, created by gas volume ruptures from a fluid–air interface.

Time-frequency analysis of acoustic signals in the audio-frequency range generated during Hadfield's steel friction

NASA Astrophysics Data System (ADS)

Dobrynin, S. A.; Kolubaev, E. A.; Smolin, A. Yu.; Dmitriev, A. I.; Psakhie, S. G.

2010-07-01

Time-frequency analysis of sound waves detected by a microphone during the friction of Hadfield’s steel has been performed using wavelet transform and window Fourier transform methods. This approach reveals a relationship between the appearance of quasi-periodic intensity outbursts in the acoustic response signals and the processes responsible for the formation of wear products. It is shown that the time-frequency analysis of acoustic emission in a tribosystem can be applied, along with traditional approaches, to studying features in the wear and friction process.

An efficient robust sound classification algorithm for hearing aids.

PubMed

Nordqvist, Peter; Leijon, Arne

2004-06-01

An efficient robust sound classification algorithm based on hidden Markov models is presented. The system would enable a hearing aid to automatically change its behavior for differing listening environments according to the user's preferences. This work attempts to distinguish between three listening environment categories: speech in traffic noise, speech in babble, and clean speech, regardless of the signal-to-noise ratio. The classifier uses only the modulation characteristics of the signal. The classifier ignores the absolute sound pressure level and the absolute spectrum shape, resulting in an algorithm that is robust against irrelevant acoustic variations. The measured classification hit rate was 96.7%-99.5% when the classifier was tested with sounds representing one of the three environment categories included in the classifier. False-alarm rates were 0.2%-1.7% in these tests. The algorithm is robust and efficient and consumes a small amount of instructions and memory. It is fully possible to implement the classifier in a DSP-based hearing instrument.

Phenotypic integration and the evolution of signal repertoires: A case study of treefrog acoustic communication.

PubMed

Reichert, Michael S; Höbel, Gerlinde

2018-03-01

Animal signals are inherently complex phenotypes with many interacting parts combining to elicit responses from receivers. The pattern of interrelationships between signal components reflects the extent to which each component is expressed, and responds to selection, either in concert with or independently of others. Furthermore, many species have complex repertoires consisting of multiple signal types used in different contexts, and common morphological and physiological constraints may result in interrelationships extending across the multiple signals in species' repertoires. The evolutionary significance of interrelationships between signal traits can be explored within the framework of phenotypic integration, which offers a suite of quantitative techniques to characterize complex phenotypes. In particular, these techniques allow for the assessment of modularity and integration, which describe, respectively, the extent to which sets of traits covary either independently or jointly. Although signal and repertoire complexity are thought to be major drivers of diversification and social evolution, few studies have explicitly measured the phenotypic integration of signals to investigate the evolution of diverse communication systems. We applied methods from phenotypic integration studies to quantify integration in the two primary vocalization types (advertisement and aggressive calls) in the treefrogs Hyla versicolor , Hyla cinerea, and Dendropsophus ebraccatus . We recorded male calls and calculated standardized phenotypic variance-covariance ( P ) matrices for characteristics within and across call types. We found significant integration across call types, but the strength of integration varied by species and corresponded with the acoustic similarity of the call types within each species. H. versicolor had the most modular advertisement and aggressive calls and the least acoustically similar call types. Additionally, P was robust to changing social competition

Predict or classify: The deceptive role of time-locking in brain signal classification

NASA Astrophysics Data System (ADS)

Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Interspecific visual signalling in animals and plants: a functional classification.

PubMed

Caro, Tim; Allen, William L

2017-07-05

Organisms frequently gain advantages when they engage in signalling with individuals of other species. Here, we provide a functionally structured framework of the great variety of interspecific visual signals seen in nature, and then describe the different signalling mechanisms that have evolved in response to each of these functional requirements. We propose that interspecific visual signalling can be divided into six major functional categories: anti-predator, food acquisition, anti-parasite, host acquisition, reproductive and agonistic signalling, with each function enabled by several distinct mechanisms. We support our classification by reviewing the ecological and behavioural drivers of interspecific signalling in animals and plants, principally focusing on comparative studies that address large-scale patterns of diversity. Collating diverse examples of interspecific signalling into an organized set of functional and mechanistic categories places anachronistic behavioural and morphological labels in fresh context, clarifies terminology and redirects research effort towards understanding environmental influences driving interspecific signalling in nature.This article is part of the themed issue 'Animal coloration: production, perception, function and application'. © 2017 The Author(s).

Brain Activation Patterns in Response to Conspecific and Heterospecific Social Acoustic Signals in Female Plainfin Midshipman Fish, Porichthys notatus.

PubMed

Mohr, Robert A; Chang, Yiran; Bhandiwad, Ashwin A; Forlano, Paul M; Sisneros, Joseph A

2018-01-01

While the peripheral auditory system of fish has been well studied, less is known about how the fish's brain and central auditory system process complex social acoustic signals. The plainfin midshipman fish, Porichthys notatus, has become a good species for investigating the neural basis of acoustic communication because the production and reception of acoustic signals is paramount for this species' reproductive success. Nesting males produce long-duration advertisement calls that females detect and localize among the noise in the intertidal zone to successfully find mates and spawn. How female midshipman are able to discriminate male advertisement calls from environmental noise and other acoustic stimuli is unknown. Using the immediate early gene product cFos as a marker for neural activity, we quantified neural activation of the ascending auditory pathway in female midshipman exposed to conspecific advertisement calls, heterospecific white seabass calls, or ambient environment noise. We hypothesized that auditory hindbrain nuclei would be activated by general acoustic stimuli (ambient noise and other biotic acoustic stimuli) whereas auditory neurons in the midbrain and forebrain would be selectively activated by conspecific advertisement calls. We show that neural activation in two regions of the auditory hindbrain, i.e., the rostral intermediate division of the descending octaval nucleus and the ventral division of the secondary octaval nucleus, did not differ via cFos immunoreactive (cFos-ir) activity when exposed to different acoustic stimuli. In contrast, female midshipman exposed to conspecific advertisement calls showed greater cFos-ir in the nucleus centralis of the midbrain torus semicircularis compared to fish exposed only to ambient noise. No difference in cFos-ir was observed in the torus semicircularis of animals exposed to conspecific versus heterospecific calls. However, cFos-ir was greater in two forebrain structures that receive auditory input, i

Acoustic transistor: Amplification and switch of sound by sound

NASA Astrophysics Data System (ADS)

Liang, Bin; Kan, Wei-wei; Zou, Xin-ye; Yin, Lei-lei; Cheng, Jian-chun

2014-08-01

We designed an acoustic transistor to manipulate sound in a manner similar to the manipulation of electric current by its electrical counterpart. The acoustic transistor is a three-terminal device with the essential ability to use a small monochromatic acoustic signal to control a much larger output signal within a broad frequency range. The output and controlling signals have the same frequency, suggesting the possibility of cascading the structure to amplify an acoustic signal. Capable of amplifying and switching sound by sound, acoustic transistors have various potential applications and may open the way to the design of conceptual devices such as acoustic logic gates.

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

PubMed Central

Rana, Mohit; Prasad, Vinod A.; Guan, Cuntai; Birbaumer, Niels; Sitaram, Ranganatha

2016-01-01

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity. PMID:27467528

Analysis of the acoustic spectral signature of prosthetic heart valves in patients experiencing atrial fibrillation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scott, D.D.; Jones, H.E.

1994-05-06

Prosthetic heart valves have increased the life span of many patients with life threatening heart conditions. These valves have proven extremely reliable adding years to what would have been weeks to a patient`s life. Prosthetic valves, like the heart however, can suffer from this constant work load. A small number of valves have experienced structural fractures of the outlet strut due to fatigue. To study this problem a non-intrusive method to classify valves has been developed. By extracting from an acoustic signal the opening sounds which directly contain information from the outlet strut and then developing features which are suppliedmore » to an adaptive classification scheme (neural network) the condition of the valve can be determined. The opening sound extraction process has proved to be a classification problem itself. Due to the uniqueness of each heart and the occasional irregularity of the acoustic pattern it is often questionable as to the integrity of a given signal (beat), especially one occurring during an irregular beat pattern. A common cause of these irregular patterns is a condition known as atrial fibrillation, a prevalent arrhythmia among patients with prosthetic hear valves. Atrial fibrillation is suspected when the ECG shows no obvious P-waves. The atria do not contract and relax correctly to help contribute to ventricular filling during a normal cardiac cycle. Sometimes this leads to irregular patterns in the acoustic data. This study compares normal beat patterns to irregular patterns of the same heart. By analyzing the spectral content of the beats it can be determined whether or not these irregular patterns can contribute to the classification of a heart valve or if they should be avoided. The results have shown that the opening sounds which occur during irregular beat patterns contain the same spectral information as the opening which occur during a normal beat pattern of the same heart and these beats can be used for classification.« less

Algorithm for heart rate extraction in a novel wearable acoustic sensor

PubMed Central

Imtiaz, Syed Anas; Aguilar–Pelaez, Eduardo; Rodriguez–Villegas, Esther

2015-01-01

Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds – S1 and S2 – that can be used to determine the heart rate. The conventional method of acoustic signal acquisition involves placing the sound sensor at the chest where this sound is most audible. Presented is a novel algorithm for the detection of S1 and S2 heart sounds and the use of them to extract the heart rate from signals acquired by a small sensor placed at the neck. This algorithm achieves an accuracy of 90.73 and 90.69%, with respect to heart rate value provided by two commercial devices, evaluated on more than 38 h of data acquired from ten different subjects during sleep in a pilot clinical study. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. The algorithm in this study used signals from a sensor designed to monitor breathing. This shows that the same sensor and signal can be used to monitor both breathing and heart rate, making it highly useful for long-term wearable vital signs monitoring. PMID:26609401

Airborne DoA estimation of gunshot acoustic signals using drones with application to sniper localization systems

NASA Astrophysics Data System (ADS)

Fernandes, Rigel P.; Ramos, António L. L.; Apolinário, José A.

2017-05-01

Shooter localization systems have been subject of a growing attention lately owing to its wide span of possible applications, e.g., civil protection, law enforcement, and support to soldiers in missions where snipers might pose a serious threat. These devices are based on the processing of electromagnetic or acoustic signatures associated with the firing of a gun. This work is concerned with the latter, where the shooter's position can be obtained based on the estimation of the direction-of-arrival (DoA) of the acoustic components of a gunshot signal (muzzle blast and shock wave). A major limitation of current commercially available acoustic sniper localization systems is the impossibility of finding the shooter's position when one of these acoustic signatures is not detected. This is very likely to occur in real-life situations, especially when the microphones are not in the field of view of the shockwave or when the presence of obstacles like buildings can prevent a direct-path to sensors. This work addresses the problem of DoA estimation of the muzzle blast using a planar array of sensors deployed in a drone. Results supported by actual gunshot data from a realistic setup are very promising and pave the way for the development of enhanced sniper localization systems featuring two main advantages over stationary ones: (1) wider surveillance area; and (2) increased likelihood of a direct-path detection of at least one of the gunshot signals, thereby adding robustness and reliability to the system.

Automated Acoustic Identification of Bats

DTIC Science & Technology

2011-10-01

signals. Other signal sources contribute to the overall acoustic soundscape and interfere with discerning the bat calls from the background signals. In...noises, and the changing soundscape from moving can all exceed at least parts of the signal amplitude of bat calls. The ability to track the trend...results, full-spectrum data also provides an effective voucher for interpretation of the full acoustic soundscape at the time of the recording. Figure

Acoustic logic gates and Boolean operation based on self-collimating acoustic beams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Ting; Xu, Jian-yi; Cheng, Ying, E-mail: chengying@nju.edu.cn

2015-03-16

The reveal of self-collimation effect in two-dimensional (2D) photonic or acoustic crystals has opened up possibilities for signal manipulation. In this paper, we have proposed acoustic logic gates based on the linear interference of self-collimated beams in 2D sonic crystals (SCs) with line-defects. The line defects on the diagonal of the 2D square SCs are actually functioning as a 3 dB splitter. By adjusting the phase difference between two input signals, the basic Boolean logic functions such as XOR, OR, AND, and NOT are achieved both theoretically and experimentally. Due to the non-diffracting property of self-collimation beams, more complex Boolean logicmore » and algorithms such as NAND, NOR, and XNOR can be realized by cascading the basic logic gates. The achievement of acoustic logic gates and Boolean operation provides a promising approach for acoustic signal computing and manipulations.« less

Transmission acoustic microscopy investigation

NASA Astrophysics Data System (ADS)

Maev, Roman; Kolosov, Oleg; Levin, Vadim; Lobkis, Oleg

The nature of acoustic contrast, i.e. the connection of the amplitude and phase of the output signal of the acoustic microscope with the local values of the acoustic parameters of the sample (density, elasticity, viscosity) is a central problem of acoustic microscopy. A considerable number of studies have been devoted to the formation of the output signal of the reflection scanning acoustic microscope. For the transmission acoustic microscope (TAM) this problem has remained almost unstudied. Experimental investigation of the confocal system of the TAM was carried out on an independently manufactured laboratory mockup of the TAM with the working frequency of the 420 MHz. Acoustic lenses with the radius of curvature of about 500 microns and aperture angle of 45 deg were polished out in the end faces of two cylindrical sound conductors made from Al2O3 single crystals with an axis parallel to the axis C of the crystal (the length of the sound conductor is 20 mm; diameter, 6 mm). At the end faces of the sound conductor, opposite to the lenses, CdS transducers with a diameter of 2 mm were disposed. The electric channel of the TAM provided a possibility for registering the amplitude of the microscope output signal in the case of the dynamic range of the 50 dB.

A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees.

PubMed

Li, Xiangxin; Samuel, Oluwarotimi Williams; Zhang, Xu; Wang, Hui; Fang, Peng; Li, Guanglin

2017-01-07

Most of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses. Four transhumeral amputees without any form of neurological disease were recruited in the experiments. Five motion classes including hand-open, hand-close, wrist-pronation, wrist-supination, and no-movement were specified. During the motion performances, sEMG and EEG signals were simultaneously acquired from the skin surface and scalp of the amputees, respectively. The two types of signals were independently preprocessed and then combined as a parallel control input. Four time-domain features were extracted and fed into a classifier trained by the Linear Discriminant Analysis (LDA) algorithm for motion recognition. In addition, channel selections were performed by using the Sequential Forward Selection (SFS) algorithm to optimize the performance of the proposed method. The classification performance achieved by the fusion of sEMG and EEG signals was significantly better than that obtained by single signal source of either sEMG or EEG. An increment of more than 14% in classification accuracy was achieved when using a combination of 32-channel sEMG and 64-channel EEG. Furthermore, based on the SFS algorithm, two optimized electrode arrangements (10-channel sEMG + 10-channel EEG, 10-channel sEMG + 20-channel

NW-MILO Acoustic Data Collection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzner, Shari; Myers, Joshua R.; Maxwell, Adam R.

2010-02-17

There is an enduring requirement to improve our ability to detect potential threats and discriminate these from the legitimate commercial and recreational activity ongoing in the nearshore/littoral portion of the maritime domain. The Northwest Maritime Information and Littoral Operations (NW-MILO) Program at PNNL’s Coastal Security Institute in Sequim, Washington is establishing a methodology to detect and classify these threats - in part through developing a better understanding of acoustic signatures in a near-shore environment. The purpose of the acoustic data collection described here is to investigate the acoustic signatures of small vessels. The data is being recorded continuously, 24 hoursmore » a day, along with radar track data and imagery. The recording began in August 2008, and to date the data contains tens of thousands of signals from small vessels recorded in a variety of environmental conditions. The quantity and variety of this data collection, with the supporting imagery and radar track data, makes it particularly useful for the development of robust acoustic signature models and advanced algorithms for signal classification and information extraction. The underwater acoustic sensing system is part of a multi-modal sensing system that is operating near the mouth of Sequim Bay. Sequim Bay opens onto the Straight of Juan de Fuca, which contains part of the border between the U.S. and Canada. Table 1 lists the specific components used for the NW-MILO system. The acoustic sensor is a hydrophone permanently deployed at a mean depth of about 3 meters. In addition to a hydrophone, the other sensors in the system are a marine radar, an electro-optical (EO) camera and an infra-red (IR) camera. The radar is integrated with a vessel tracking system (VTS) that provides position, speed and heading information. The data from all the sensors is recorded and saved to a central server. The data has been validated in terms of its usability for characterizing the

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

PubMed

Siu, Ho Chit; Shah, Julie A; Stirling, Leia A

2016-10-25

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

PubMed Central

Siu, Ho Chit; Shah, Julie A.; Stirling, Leia A.

2016-01-01

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. PMID:27792155

A micro-Doppler sonar for acoustic surveillance in sensor networks

NASA Astrophysics Data System (ADS)

Zhang, Zhaonian

Wireless sensor networks have been employed in a wide variety of applications, despite the limited energy and communication resources at each sensor node. Low power custom VLSI chips implementing passive acoustic sensing algorithms have been successfully integrated into an acoustic surveillance unit and demonstrated for detection and location of sound sources. In this dissertation, I explore active and passive acoustic sensing techniques, signal processing and classification algorithms for detection and classification in a multinodal sensor network environment. I will present the design and characterization of a continuous-wave micro-Doppler sonar to image objects with articulated moving components. As an example application for this system, we use it to image gaits of humans and four-legged animals. I will present the micro-Doppler gait signatures of a walking person, a dog and a horse. I will discuss the resolution and range of this micro-Doppler sonar and use experimental results to support the theoretical analyses. In order to reduce the data rate and make the system amenable to wireless sensor networks, I will present a second micro-Doppler sonar that uses bandpass sampling for data acquisition. Speech recognition algorithms are explored for biometric identifications from one's gait, and I will present and compare the classification performance of the two systems. The acoustic micro-Doppler sonar design and biometric identification results are the first in the field as the previous work used either video camera or microwave technology. I will also review bearing estimation algorithms and present results of applying these algorithms for bearing estimation and tracking of moving vehicles. Another major source of the power consumption at each sensor node is the wireless interface. To address the need of low power communications in a wireless sensor network, I will also discuss the design and implementation of ultra wideband transmitters in a three dimensional

Impact-acoustics inspection of tile-wall bonding integrity via wavelet transform and hidden Markov models

NASA Astrophysics Data System (ADS)

Luk, B. L.; Liu, K. P.; Tong, F.; Man, K. F.

2010-05-01

The impact-acoustics method utilizes different information contained in the acoustic signals generated by tapping a structure with a small metal object. It offers a convenient and cost-efficient way to inspect the tile-wall bonding integrity. However, the existence of the surface irregularities will cause abnormal multiple bounces in the practical inspection implementations. The spectral characteristics from those bounces can easily be confused with the signals obtained from different bonding qualities. As a result, it will deteriorate the classic feature-based classification methods based on frequency domain. Another crucial difficulty posed by the implementation is the additive noise existing in the practical environments that may also cause feature mismatch and false judgment. In order to solve this problem, the work described in this paper aims to develop a robust inspection method that applies model-based strategy, and utilizes the wavelet domain features with hidden Markov modeling. It derives a bonding integrity recognition approach with enhanced immunity to surface roughness as well as the environmental noise. With the help of the specially designed artificial sample slabs, experiments have been carried out with impact acoustic signals contaminated by real environmental noises acquired under practical inspection background. The results are compared with those using classic method to demonstrate the effectiveness of the proposed method.

Acoustic Tomography of the Atmospheric Surface Layer

DTIC Science & Technology

2014-11-28

Report Title Acoustic tomography of the atmospheric surface layer (ASL) is based on the measurements of the travel times of sound propagation between...SECURITY CLASSIFICATION OF: Acoustic tomography of the atmospheric surface layer (ASL) is based on the measurements of the travel times of sound ...organ. In the case of acoustic tomography of the atmospheric surface layer (ASL), the travel times of sound propagation between speakers and

Acoustic emission source localization based on distance domain signal representation

NASA Astrophysics Data System (ADS)

Gawronski, M.; Grabowski, K.; Russek, P.; Staszewski, W. J.; Uhl, T.; Packo, P.

2016-04-01

Acoustic emission is a vital non-destructive testing technique and is widely used in industry for damage detection, localisation and characterization. The latter two aspects are particularly challenging, as AE data are typically noisy. What is more, elastic waves generated by an AE event, propagate through a structural path and are significantly distorted. This effect is particularly prominent for thin elastic plates. In these media the dispersion phenomenon results in severe localisation and characterization issues. Traditional Time Difference of Arrival methods for localisation techniques typically fail when signals are highly dispersive. Hence, algorithms capable of dispersion compensation are sought. This paper presents a method based on the Time - Distance Domain Transform for an accurate AE event localisation. The source localisation is found through a minimization problem. The proposed technique focuses on transforming the time signal to the distance domain response, which would be recorded at the source. Only, basic elastic material properties and plate thickness are used in the approach, avoiding arbitrary parameters tuning.

Acoustic, genetic and morphological variations within the katydid Gampsocleis sedakovii (Orthoptera, Tettigonioidea)

PubMed Central

Zhang, Xue; Wen, Ming; Li, Junjian; Zhu, Hui; Wang, Yinliang; Ren, Bingzhong

2015-01-01

Abstract In an attempt to explain the variation within this species and clarify the subspecies classification, an analysis of the genetic, calling songs, and morphological variations within the species Gampsocleis sedakovii is presented from Inner Mongolia, China. Recordings were compared of the male calling songs and analysis performed of selected acoustic variables. This analysis is combined with sequencing of mtDNA - COI and examination of morphological traits to perform cluster analyses. The trees constructed from different datasets were structurally similar, bisecting the six geographical populations studied. Based on two large branches in the analysis, the species Gampsocleis sedakovii was partitioned into two subspecies, Gampsocleis sedakovii sedakovii (Fischer von Waldheim, 1846) and Gampsocleis sedakovii obscura (Walker, 1869). Comparing all the traits, the individual of Elunchun (ELC) was the intermediate type in this species according to the acoustic, genetic, and morphological characteristics. This study provides evidence for insect acoustic signal divergence and the process of subspeciation. PMID:26692795

Experimental Research Into Generation of Acoustic Emission Signals in the Process of Friction of Hadfield Steel Single Crystals

NASA Astrophysics Data System (ADS)

Lychagin, D. V.; Filippov, A. V.; Novitskaia, O. S.; Kolubaev, E. A.; Sizova, O. V.

2016-08-01

The results of experimental research into dry sliding friction of Hadfield steel single crystals involving registration of acoustic emission are presented in the paper. The images of friction surfaces of Hadfield steel single crystals and wear grooves of the counterbody surface made after completion of three serial experiments conducted under similar conditions and friction regimes are given. The relation of the acoustic emission waveform envelope to the changing friction factor is revealed. Amplitude-frequency characteristics of acoustic emission signal frames are determined on the base of Fast Fourier Transform and Short Time Fourier Transform during the run-in stage of tribounits and in the process of stable friction.

Third International Conference on Acoustic Communication by Animals

DTIC Science & Technology

2011-09-30

communications Invited Speakers Peter Tyack cetacean communications Christopher Clark acoustic environment of whales Whitlow Au sound detection and...echolocation by dolphins Magnus Wahlberg sperm whale acoustics Robert Dooling bird hearing Ronald Hoy communication strategies in insects Peter Narins...frogs (6). Topics covered included cognition/language; song and call classification; rule learning; acoustic ecology; communication in noisy

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.

PubMed

Zhao, Weixiang; Sankaran, Shankar; Ibáñez, Ana M; Dandekar, Abhaya M; Davis, Cristina E

2009-08-04

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

Acoustic communication in plant-animal interactions.

PubMed

Schöner, Michael G; Simon, Ralph; Schöner, Caroline R

2016-08-01

Acoustic communication is widespread and well-studied in animals but has been neglected in other organisms such as plants. However, there is growing evidence for acoustic communication in plant-animal interactions. While knowledge about active acoustic signalling in plants (i.e. active sound production) is still in its infancy, research on passive acoustic signalling (i.e. reflection of animal sounds) revealed that bat-dependent plants have adapted to the bats' echolocation systems by providing acoustic reflectors to attract their animal partners. Understanding the proximate mechanisms and ultimate causes of acoustic communication will shed light on an underestimated dimension of information transfer between plants and animals. Copyright © 2016 Elsevier Ltd. All rights reserved.

Wavefront modulation and subwavelength diffractive acoustics with an acoustic metasurface.

PubMed

Xie, Yangbo; Wang, Wenqi; Chen, Huanyang; Konneker, Adam; Popa, Bogdan-Ioan; Cummer, Steven A

2014-11-24

Metasurfaces are a family of novel wavefront-shaping devices with planar profile and subwavelength thickness. Acoustic metasurfaces with ultralow profile yet extraordinary wave manipulating properties would be highly desirable for improving the performance of many acoustic wave-based applications. However, designing acoustic metasurfaces with similar functionality to their electromagnetic counterparts remains challenging with traditional metamaterial design approaches. Here we present a design and realization of an acoustic metasurface based on tapered labyrinthine metamaterials. The demonstrated metasurface can not only steer an acoustic beam as expected from the generalized Snell's law, but also exhibits various unique properties such as conversion from propagating wave to surface mode, extraordinary beam-steering and apparent negative refraction through higher-order diffraction. Such designer acoustic metasurfaces provide a new design methodology for acoustic signal modulation devices and may be useful for applications such as acoustic imaging, beam steering, ultrasound lens design and acoustic surface wave-based applications.

The effect of climate on acoustic signals: does atmospheric sound absorption matter for bird song and bat echolocation?

PubMed

Snell-Rood, Emilie C

2012-02-01

The divergence of signals along ecological gradients may lead to speciation. The current research tests the hypothesis that variation in sound absorption selects for divergence in acoustic signals along climatic gradients, which has implications for understanding not only diversification, but also how organisms may respond to climate change. Because sound absorption varies with temperature, humidity, and the frequency of sound, individuals or species may vary signal structure with changes in climate over space or time. In particular, signals of lower frequency, narrower bandwidth, and longer duration should be more detectable in environments with high sound absorption. Using both North American wood warblers (Parulidae) and bats of the American Southwest, this work found evidence of associations between signal structure and sound absorption. Warbler species with higher mean absorption across their range were more likely to have narrow bandwidth songs. Bat species found in higher absorption habitats were more likely to have lower frequency echolocation calls. In addition, bat species changed echolocation call structure across seasons, using longer duration, lower frequency calls in the higher absorption rainy season. These results suggest that signals may diverge along climatic gradients due to variation in sound absorption, although the effects of absorption are modest. © 2012 Acoustical Society of America

Punch stretching process monitoring using acoustic emission signal analysis. II - Application of frequency domain deconvolution

NASA Technical Reports Server (NTRS)

Liang, Steven Y.; Dornfeld, David A.; Nickerson, Jackson A.

1987-01-01

The coloring effect on the acoustic emission signal due to the frequency response of the data acquisition/processing instrumentation may bias the interpretation of AE signal characteristics. In this paper, a frequency domain deconvolution technique, which involves the identification of the instrumentation transfer functions and multiplication of the AE signal spectrum by the inverse of these system functions, has been carried out. In this way, the change in AE signal characteristics can be better interpreted as the result of the change in only the states of the process. Punch stretching process was used as an example to demonstrate the application of the technique. Results showed that, through the deconvolution, the frequency characteristics of AE signals generated during the stretching became more distinctive and can be more effectively used as tools for process monitoring.

Automated Classification of Medical Percussion Signals for the Diagnosis of Pulmonary Injuries

NASA Astrophysics Data System (ADS)

Bhuiyan, Md Moinuddin

Used for centuries in the clinical practice, audible percussion is a method of eliciting sounds by areas of the human body either by finger tips or by a percussion hammer. Despite its advantages, pulmonary diagnostics by percussion is still highly subjective, depends on the physician's skills, and requires quiet surroundings. Automation of this well-established technique could help amplify its existing merits while removing the above drawbacks. In this study, an attempt is made to automatically decompose clinical percussion signals into a sum of Exponentially Damped Sinusoids (EDS) using Matrix Pencil Method, which in this case form a more natural basis than Fourier harmonics and thus allow for a more robust representation of the signal in the parametric space. It is found that some EDS represent transient oscillation modes of the thorax/abdomen excited by the percussion event, while others are associated with the noise. It is demonstrated that relatively few EDS are usually enough to accurately reconstruct the original signal. It is shown that combining the frequency and damping parameters of these most significant EDS allows for efficient classification of percussion signals into the two main types historically known as "resonant" and "tympanic". This classification ability can provide a basis for the automated objective diagnostics of various pulmonary pathologies including pneumothorax.

Signal identification in acoustic emission monitoring of fatigue cracking in steel bridges

NASA Astrophysics Data System (ADS)

Yu, Jianguo P.; Ziehl, Paul; Pollock, Adrian

2012-04-01

Signal identification including noise filtering and reduction of acquired signals is needed to achieve efficient and accurate data interpretation for remote acoustic emission (AE) monitoring of in-service steel bridges. Noise filtering may ensure that genuine hits from crack growth are involved in the estimation of fatigue damage and remaining fatigue life. Reduction of the data quantity is desirable for the sensing system to conserve energy in the data transmission and processing procedures. Identification and categorization of acquired signals is a promising approach to effectively filter and reduce AE data in the application of bridge monitoring. In this study an investigation on waveform features (time domain and frequency domain) and relevant filters is carried out using the results from AE monitored fatigue tests. It is verified that duration-amplitude (D-A) filters are effective to discriminate against noise for results of steel fatigue tests. The study is helpful to find an appropriate AE data filtering protocol for field implementations.

Combined Use of Standard and Throat Microphones for Measurement of Acoustic Voice Parameters and Voice Categorization.

PubMed

Uloza, Virgilijus; Padervinskis, Evaldas; Uloziene, Ingrida; Saferis, Viktoras; Verikas, Antanas

2015-09-01

The aim of the present study was to evaluate the reliability of the measurements of acoustic voice parameters obtained simultaneously using oral and contact (throat) microphones and to investigate utility of combined use of these microphones for voice categorization. Voice samples of sustained vowel /a/ obtained from 157 subjects (105 healthy and 52 pathological voices) were recorded in a soundproof booth simultaneously through two microphones: oral AKG Perception 220 microphone (AKG Acoustics, Vienna, Austria) and contact (throat) Triumph PC microphone (Clearer Communications, Inc, Burnaby, Canada) placed on the lamina of thyroid cartilage. Acoustic voice signal data were measured for fundamental frequency, percent of jitter and shimmer, normalized noise energy, signal-to-noise ratio, and harmonic-to-noise ratio using Dr. Speech software (Tiger Electronics, Seattle, WA). The correlations of acoustic voice parameters in vocal performance were statistically significant and strong (r = 0.71-1.0) for the entire functional measurements obtained for the two microphones. When classifying into healthy-pathological voice classes, the oral-shimmer revealed the correct classification rate (CCR) of 75.2% and the throat-jitter revealed CCR of 70.7%. However, combination of both throat and oral microphones allowed identifying a set of three voice parameters: throat-signal-to-noise ratio, oral-shimmer, and oral-normalized noise energy, which provided the CCR of 80.3%. The measurements of acoustic voice parameters using a combination of oral and throat microphones showed to be reliable in clinical settings and demonstrated high CCRs when distinguishing the healthy and pathological voice patient groups. Our study validates the suitability of the throat microphone signal for the task of automatic voice analysis for the purpose of voice screening. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Effect of reflected and refracted signals on coherent underwater acoustic communication: results from the Kauai experiment (KauaiEx 2003).

PubMed

Rouseff, Daniel; Badiey, Mohsen; Song, Aijun

2009-11-01

The performance of a communications equalizer is quantified in terms of the number of acoustic paths that are treated as usable signal. The analysis uses acoustical and oceanographic data collected off the Hawaiian Island of Kauai. Communication signals were measured on an eight-element vertical array at two different ranges, 1 and 2 km, and processed using an equalizer based on passive time-reversal signal processing. By estimating the Rayleigh parameter, it is shown that all paths reflected by the sea surface at both ranges undergo incoherent scattering. It is demonstrated that some of these incoherently scattered paths are still useful for coherent communications. At range of 1 km, optimal communications performance is achieved when six acoustic paths are retained and all paths with more than one reflection off the sea surface are rejected. Consistent with a model that ignores loss from near-surface bubbles, the performance improves by approximately 1.8 dB when increasing the number of retained paths from four to six. The four-path results though are more stable and require less frequent channel estimation. At range of 2 km, ray refraction is observed and communications performance is optimal when some paths with two sea-surface reflections are retained.

Acoustic Transmitters for Underwater Neutrino Telescopes

PubMed Central

Ardid, Miguel; Martínez-Mora, Juan A.; Bou-Cabo, Manuel; Larosa, Giuseppina; Adrián-Martínez, Silvia; Llorens, Carlos D.

2012-01-01

In this paper acoustic transmitters that were developed for use in underwater neutrino telescopes are presented. Firstly, an acoustic transceiver has been developed as part of the acoustic positioning system of neutrino telescopes. These infrastructures are not completely rigid and require a positioning system in order to monitor the position of the optical sensors which move due to sea currents. To guarantee a reliable and versatile system, the transceiver has the requirements of reduced cost, low power consumption, high pressure withstanding (up to 500 bars), high intensity for emission, low intrinsic noise, arbitrary signals for emission and the capacity of acquiring and processing received signals. Secondly, a compact acoustic transmitter array has been developed for the calibration of acoustic neutrino detection systems. The array is able to mimic the signature of ultra-high-energy neutrino interaction in emission directivity and signal shape. The technique of parametric acoustic sources has been used to achieve the proposed aim. The developed compact array has practical features such as easy manageability and operation. The prototype designs and the results of different tests are described. The techniques applied for these two acoustic systems are so powerful and versatile that may be of interest in other marine applications using acoustic transmitters. PMID:22666022

Multi-label spacecraft electrical signal classification method based on DBN and random forest

PubMed Central

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data. PMID:28486479

Multi-label spacecraft electrical signal classification method based on DBN and random forest.

PubMed

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data.

Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor

NASA Astrophysics Data System (ADS)

Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

2014-05-01

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor.

PubMed

Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

2014-05-01

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals.

PubMed

Gupta, Anubha; Singh, Pushpendra; Karlekar, Mandar

2018-05-01

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species.

PubMed

Ludeña-Choez, Jimmy; Quispe-Soncco, Raisa; Gallardo-Antolín, Ascensión

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species

PubMed Central

Quispe-Soncco, Raisa

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. PMID:28628630

Photo-acoustic and video-acoustic methods for sensing distant sound sources

NASA Astrophysics Data System (ADS)

Slater, Dan; Kozacik, Stephen; Kelmelis, Eric

2017-05-01

Long range telescopic video imagery of distant terrestrial scenes, aircraft, rockets and other aerospace vehicles can be a powerful observational tool. But what about the associated acoustic activity? A new technology, Remote Acoustic Sensing (RAS), may provide a method to remotely listen to the acoustic activity near these distant objects. Local acoustic activity sometimes weakly modulates the ambient illumination in a way that can be remotely sensed. RAS is a new type of microphone that separates an acoustic transducer into two spatially separated components: 1) a naturally formed in situ acousto-optic modulator (AOM) located within the distant scene and 2) a remote sensing readout device that recovers the distant audio. These two elements are passively coupled over long distances at the speed of light by naturally occurring ambient light energy or other electromagnetic fields. Stereophonic, multichannel and acoustic beam forming are all possible using RAS techniques and when combined with high-definition video imagery it can help to provide a more cinema like immersive viewing experience. A practical implementation of a remote acousto-optic readout device can be a challenging engineering problem. The acoustic influence on the optical signal is generally weak and often with a strong bias term. The optical signal is further degraded by atmospheric seeing turbulence. In this paper, we consider two fundamentally different optical readout approaches: 1) a low pixel count photodiode based RAS photoreceiver and 2) audio extraction directly from a video stream. Most of our RAS experiments to date have used the first method for reasons of performance and simplicity. But there are potential advantages to extracting audio directly from a video stream. These advantages include the straight forward ability to work with multiple AOMs (useful for acoustic beam forming), simpler optical configurations, and a potential ability to use certain preexisting video recordings. However

Method and apparatus of spectro-acoustically enhanced ultrasonic detection for diagnostics

DOEpatents

Vo-Dinh, Tuan; Norton, Stephen J.

2001-01-01

An apparatus for detecting a discontinuity in a material includes a source of electromagnetic radiation has a wavelength and an intensity sufficient to induce an enhancement in contrast between a manifestation of an acoustic property in the material and of the acoustic property in the discontinuity, as compared to when the material is not irradiated by the electromagnetic radiation. An acoustic emitter directs acoustic waves to the discontinuity in the material. The acoustic waves have a sensitivity to the acoustic property. An acoustic receiver receives the acoustic waves generated by the acoustic emitter after the acoustic waves have interacted with the material and the discontinuity. The acoustic receiver also generates a signal representative of the acoustic waves received by the acoustic receiver. A processor, in communication with the acoustic receiver and responsive to the signal generated by the acoustic receiver, is programmed to generate informational output about the discontinuity based on the signal generated by the acoustic receiver.

Portable Multi Hydrophone Array for Field and Laboratory Measurements of Odontocete Acoustic Signals

DTIC Science & Technology

2015-09-30

bottlenose dolphin and a false killer whale as well as other species such as the Risso’s dolphin . We are currently comparing various file types and methods...echolocation abilities of a Risso’s dolphin are currently being reviewed for publication. 3 The hook detection study recordings are being analyzed for basic...element array in order to obtain horizontal and vertical beam patterns of acoustic signals of a false killer whale and a bottlenose dolphin . The data

Cavitation controlled acoustic probe for fabric spot cleaning and moisture monitoring

DOEpatents

Sheen, Shuh-Haw; Chien, Hual-Te; Raptis, Apostolos C.

1997-01-01

A method and apparatus are provided for monitoring a fabric. An acoustic probe generates acoustic waves relative to the fabric. An acoustic sensor, such as an accelerometer is coupled to the acoustic probe for generating a signal representative of cavitation activity in the fabric. The generated cavitation activity representative signal is processed to indicate moisture content of the fabric. A feature of the invention is a feedback control signal is generated responsive to the generated cavitation activity representative signal. The feedback control signal can be used to control the energy level of the generated acoustic waves and to control the application of a cleaning solution to the fabric.

Processing of simple and complex acoustic signals in a tonotopically organized ear

PubMed Central

Hummel, Jennifer; Wolf, Konstantin; Kössl, Manfred; Nowotny, Manuela

2014-01-01

Processing of complex signals in the hearing organ remains poorly understood. This paper aims to contribute to this topic by presenting investigations on the mechanical and neuronal response of the hearing organ of the tropical bushcricket species Mecopoda elongata to simple pure tone signals as well as to the conspecific song as a complex acoustic signal. The high-frequency hearing organ of bushcrickets, the crista acustica (CA), is tonotopically tuned to frequencies between about 4 and 70 kHz. Laser Doppler vibrometer measurements revealed a strong and dominant low-frequency-induced motion of the CA when stimulated with either pure tone or complex stimuli. Consequently, the high-frequency distal area of the CA is more strongly deflected by low-frequency-induced waves than by high-frequency-induced waves. This low-frequency dominance will have strong effects on the processing of complex signals. Therefore, we additionally studied the neuronal response of the CA to native and frequency-manipulated chirps. Again, we found a dominant influence of low-frequency components within the conspecific song, indicating that the mechanical vibration pattern highly determines the neuronal response of the sensory cells. Thus, we conclude that the encoding of communication signals is modulated by ear mechanics. PMID:25339727

Acoustic resonance phase locked photoacoustic spectrometer

DOEpatents

Pilgrim, Jeffrey S.; Bomse, David S.; Silver, Joel A.

2003-08-19

A photoacoustic spectroscopy method and apparatus for maintaining an acoustic source frequency on a sample cell resonance frequency comprising: providing an acoustic source to the sample cell to generate a photoacoustic signal, the acoustic source having a source frequency; continuously measuring detection phase of the photoacoustic signal with respect to source frequency or a harmonic thereof; and employing the measured detection phase to provide magnitude and direction for correcting the source frequency to the resonance frequency.

Circuit for echo and noise suppression of acoustic signals transmitted through a drill string

DOEpatents

Drumheller, D.S.; Scott, D.D.

1993-12-28

An electronic circuit for digitally processing analog electrical signals produced by at least one acoustic transducer is presented. In a preferred embodiment of the present invention, a novel digital time delay circuit is utilized which employs an array of First-in-First-out (FiFo) microchips. Also, a bandpass filter is used at the input to this circuit for isolating drill string noise and eliminating high frequency output. 20 figures.

Acoustic Emission Signals in Thin Plates Produced by Impact Damage

NASA Technical Reports Server (NTRS)

Prosser, William H.; Gorman, Michael R.; Humes, Donald H.

1999-01-01

Acoustic emission (AE) signals created by impact sources in thin aluminum and graphite/epoxy composite plates were analyzed. Two different impact velocity regimes were studied. Low-velocity (less than 0.21 km/s) impacts were created with an airgun firing spherical steel projectiles (4.5 mm diameter). High-velocity (1.8 to 7 km/s) impacts were generated with a two-stage light-gas gun firing small cylindrical nylon projectiles (1.5 mm diameter). Both the impact velocity and impact angle were varied. The impacts did not penetrate the aluminum plates at either low or high velocities. For high-velocity impacts in composites, there were both impacts that fully penetrated the plate as well as impacts that did not. All impacts generated very large amplitude AE signals (1-5 V at the sensor), which propagated as plate (extensional and/or flexural) modes. In the low-velocity impact studies, the signal was dominated by a large flexural mode with only a small extensional mode component detected. As the impact velocity was increased within the low velocity regime, the overall amplitudes of both the extensional and flexural modes increased. In addition, a relative increase in the amplitude of high-frequency components of the flexural mode was also observed. Signals caused by high-velocity impacts that did not penetrate the plate contained both a large extensional and flexural mode component of comparable amplitudes. The signals also contained components of much higher frequency and were easily differentiated from those caused by low-velocity impacts. An interesting phenomenon was observed in that the large flexural mode component, seen in every other case, was absent from the signal when the impact particle fully penetrated through the composite plates.

Real-time classification of signals from three-component seismic sensors using neural nets

NASA Astrophysics Data System (ADS)

Bowman, B. C.; Dowla, F.

1992-05-01

Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

Problems Associated with Statistical Pattern Recognition of Acoustic Emission Signals in a Compact Tension Fatigue Specimen

NASA Technical Reports Server (NTRS)

Hinton, Yolanda L.

1999-01-01

Acoustic emission (AE) data were acquired during fatigue testing of an aluminum 2024-T4 compact tension specimen using a commercially available AE system. AE signals from crack extension were identified and separated from noise spikes, signals that reflected from the specimen edges, and signals that saturated the instrumentation. A commercially available software package was used to train a statistical pattern recognition system to classify the signals. The software trained a network to recognize signals with a 91-percent accuracy when compared with the researcher's interpretation of the data. Reasons for the discrepancies are examined and it is postulated that additional preprocessing of the AE data to focus on the extensional wave mode and eliminate other effects before training the pattern recognition system will result in increased accuracy.

Bilinear Time-frequency Analysis for Lamb Wave Signal Detected by Electromagnetic Acoustic Transducer

NASA Astrophysics Data System (ADS)

Sun, Wenxiu; Liu, Guoqiang; Xia, Hui; Xia, Zhengwu

2018-03-01

Accurate acquisition of the detection signal travel time plays a very important role in cross-hole tomography. The experimental platform of aluminum plate under the perpendicular magnetic field is established and the bilinear time-frequency analysis methods, Wigner-Ville Distribution (WVD) and the pseudo-Wigner-Ville distribution (PWVD), are applied to analyse the Lamb wave signals detected by electromagnetic acoustic transducer (EMAT). By extracting the same frequency component of the time-frequency spectrum as the excitation frequency, the travel time information can be obtained. In comparison with traditional linear time-frequency analysis method such as short-time Fourier transform (STFT), the bilinear time-frequency analysis method PWVD is more appropriate in extracting travel time and recognizing patterns of Lamb wave.

Support-vector-machines-based multidimensional signal classification for fetal activity characterization

NASA Astrophysics Data System (ADS)

Ribes, S.; Voicu, I.; Girault, J. M.; Fournier, M.; Perrotin, F.; Tranquart, F.; Kouamé, D.

2011-03-01

Electronic fetal monitoring may be required during the whole pregnancy to closely monitor specific fetal and maternal disorders. Currently used methods suffer from many limitations and are not sufficient to evaluate fetal asphyxia. Fetal activity parameters such as movements, heart rate and associated parameters are essential indicators of the fetus well being, and no current device gives a simultaneous and sufficient estimation of all these parameters to evaluate the fetus well-being. We built for this purpose, a multi-transducer-multi-gate Doppler system and developed dedicated signal processing techniques for fetal activity parameter extraction in order to investigate fetus's asphyxia or well-being through fetal activity parameters. To reach this goal, this paper shows preliminary feasibility of separating normal and compromised fetuses using our system. To do so, data set consisting of two groups of fetal signals (normal and compromised) has been established and provided by physicians. From estimated parameters an instantaneous Manning-like score, referred to as ultrasonic score was introduced and was used together with movements, heart rate and associated parameters in a classification process using Support Vector Machines (SVM) method. The influence of the fetal activity parameters and the performance of the SVM were evaluated using the computation of sensibility, specificity, percentage of support vectors and total classification accuracy. We showed our ability to separate the data into two sets : normal fetuses and compromised fetuses and obtained an excellent matching with the clinical classification performed by physician.

System and method for sonic wave measurements using an acoustic beam source

DOEpatents

Vu, Cung Khac; Sinha, Dipen N.; Pantea, Cristian

2015-08-11

A method and system for investigating structure near a borehole are described herein. The method includes generating an acoustic beam by an acoustic source; directing at one or more azimuthal angles the acoustic beam towards a selected location in a vicinity of a borehole; receiving at one or more receivers an acoustic signal, the acoustic signal originating from a reflection or a refraction of the acoustic wave by a material at the selected location; and analyzing the received acoustic signal to characterize features of the material around the borehole.

Study of opto-acoustic communication between air and underwater carrier

NASA Astrophysics Data System (ADS)

Zong, Si-Guang; Liu, Tao; Cao, Jing; He, Qi-Yi

2018-02-01

How to solve the communication problem to the underwater target has turned into one of the subjects that the militarists of all over the world commonly concern. Laser-induced acoustic signal is a new approach for underwater acoustic source, which has much virtue such as high intensity, short pulse and broad frequency. The paper studies the opto-acoustic communication method. The acoustic signal characteristic of laser-induced breakdown is studied and corresponding theory model is systemically analyzed. The opto-acoustic communication experimental measure investigation is formed with the high power laser, water tank and high frequency hydrophone. The characteristic of acoustic signal is analyzed, such as intensity and frequency. This makes a stride for pursing the feasibility of laser-acoustic underwater communication.

Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods

ERIC Educational Resources Information Center

Liu, Boquan; Polce, Evan; Sprott, Julien C.; Jiang, Jack J.

2018-01-01

Purpose: The purpose of this study is to introduce a chaos level test to evaluate linear and nonlinear voice type classification method performances under varying signal chaos conditions without subjective impression. Study Design: Voice signals were constructed with differing degrees of noise to model signal chaos. Within each noise power, 100…

Coupled High Speed Imaging and Seismo-Acoustic Recordings of Strombolian Explosions at Etna, July 2014: Implications for Source Processes and Signal Inversions.

NASA Astrophysics Data System (ADS)

Taddeucci, J.; Del Bello, E.; Scarlato, P.; Ricci, T.; Andronico, D.; Kueppers, U.; Cannata, A.; Sesterhenn, J.; Spina, L.

2015-12-01

Seismic and acoustic surveillance is routinely performed at several persistent activity volcanoes worldwide. However, interpretation of the signals associated with explosive activity is still equivocal, due to both source variability and the intrinsically limited information carried by the waves. Comparison and cross-correlation of the geophysical quantities with other information in general and visual recording in particular is therefore actively sought. At Etna (Italy) in July 2014, short-lived Strombolian explosions ejected bomb- to lapilli-sized, molten pyroclasts at a remarkably repeatable time interval of about two seconds, offering a rare occasion to systematically investigate the seismic and acoustic fields radiated by this common volcanic source. We deployed FAMoUS (FAst, MUltiparametric Setup for the study of explosive activity) at 260 meters from the vents, recording more than 60 explosions in thermal and visible high-speed videos (50 to 500 frames per second) and broadband seismic and acoustic instruments (1 to 10000 Hz for the acoustic and from 0.01 to 30 Hz for the seismic). Analysis of this dataset highlights nonlinear relationships between the exit velocity and mass of ejecta and the amplitude and frequency of the acoustic signals. It also allows comparing different methods to estimate source depth, and to validate existing theory on the coupling of airwaves with ground motion.

Skull's acoustic attenuation and dispersion modeling on photoacoustic signal

NASA Astrophysics Data System (ADS)

Mohammadi, Leila; Behnam, Hamid; Tavakkoli, Jahan; Nasiriavanaki, Mohammadreza

2018-02-01

Despite the promising results of the recent novel transcranial photoacoustic (PA) brain imaging technology, it has been demonstrated that the presence of the skull severely affects the performance of this imaging modality. We theoretically investigate the effects of acoustic heterogeneity induced by skull on the PA signals generated from single particles, with firstly developing a mathematical model for this phenomenon and then explore experimental validation of the results. The model takes into account the frequency dependent attenuation and dispersion effects occur with wave reflection, refraction and mode conversion at the skull surfaces. Numerical simulations based on the developed model are performed for calculating the propagation of photoacoustic waves through the skull. The results show a strong agreement between simulation and ex-vivo study. The findings are as follow: The thickness of the skull is the most PA signal deteriorating factor that affects both its amplitude (attenuation) and phase (distortion). Also we demonstrated that, when the depth of target region is low and it is comparable to the skull thickness, however, the skull-induced distortion becomes increasingly severe and the reconstructed image would be strongly distorted without correcting these effects. It is anticipated that an accurate quantification and modeling of the skull transmission effects would ultimately allow for aberration correction in transcranial PA brain imaging.

Acoustic Scattering Classification of Zooplankton and Microstructure

DTIC Science & Technology

2001-09-30

As part of this investigation, we have been observing concentrations of siphonulae, a larval form of the gas-bearing zooplankton siphonophore . The...situ measurements of acoustic target strengths of siphonophores , a gas-bearing zooplankter,” ICES J. Mar. Sci. 58: 740-749. Warren, J.D., T.K

Detection and classification of subject-generated artifacts in EEG signals using autoregressive models.

PubMed

Lawhern, Vernon; Hairston, W David; McDowell, Kaleb; Westerfield, Marissa; Robbins, Kay

2012-07-15

We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts. AR model parameters are scale-invariant features that can be used to develop models of artifacts across a population. We use a support vector machine (SVM) classifier to discriminate among artifact conditions using the AR model parameters as features. Results indicate reliable classification among several different artifact conditions across subjects (approximately 94%). These results suggest that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals. Copyright © 2012 Elsevier B.V. All rights reserved.

Vibratory regime classification of infant phonation.

PubMed

Buder, Eugene H; Chorna, Lesya B; Oller, D Kimbrough; Robinson, Rebecca B

2008-09-01

Infant phonation is highly variable in many respects, including the basic vibratory patterns by which the vocal tissues create acoustic signals. Previous studies have identified the regular occurrence of nonmodal phonation types in normal infant phonation. The glottis is like many oscillating systems that, because of nonlinear relationships among the elements, may vibrate in ways representing the deterministic patterns classified theoretically within the mathematical framework of nonlinear dynamics. The infant's preverbal vocal explorations present such a variety of phonations that it may be possible to find effectively all the classes of vibration predicted by nonlinear dynamic theory. The current report defines acoustic criteria for an important subset of such vibratory regimes, and demonstrates that analysts can be trained to reliably use these criteria for a classification that includes all instances of infant phonation in the recorded corpora. The method is thus internally comprehensive in the sense that all phonations are classified, but it is not exhaustive in the sense that all vocal qualities are thereby represented. Using the methods thus developed, this study also demonstrates that the distributions of these phonation types vary significantly across sessions of recording in the first year of life, suggesting developmental changes. The method of regime classification is thus capable of tracking changes that may be indicative of maturation of the mechanism, the learning of categories of phonatory control, and the possibly varying use of vocalizations across social contexts.

Vibratory Regime Classification of Infant Phonation

PubMed Central

Buder, Eugene H.; Chorna, Lesya B.; Oller, D. Kimbrough; Robinson, Rebecca B.

2008-01-01

Infant phonation is highly variable in many respects, including the basic vibratory patterns by which the vocal tissues create acoustic signals. Previous studies have identified the regular occurrence of non-modal phonation types in normal infant phonation. The glottis is like many oscillating systems that, because of non-linear relationships among the elements, may vibrate in ways representing the deterministic patterns classified theoretically within the mathematical framework of non-linear dynamics. The infant’s pre-verbal vocal explorations present such a variety of phonations that it may be possible to find effectively all the classes of vibration predicted by non-linear dynamic theory. The current report defines acoustic criteria for an important subset of such vibratory regimes, and demonstrates that analysts can be trained to reliably use these criteria for a classification that includes all instances of infant phonation in the recorded corpora. The method is thus internally comprehensive in the sense that all phonations are classified, but it is not exhaustive in the sense that all vocal qualities are thereby represented. Using the methods thus developed, this study also demonstrates that the distributions of these phonation types vary significantly across sessions of recording in the first year of life, suggesting developmental changes. The method of regime classification is thus capable of tracking changes that may be indicative of maturation of the mechanism, the learning of categories of phonatory control, and the possibly varying use of vocalizations across social contexts. PMID:17509829

A Quantitative Analysis of Pulsed Signals Emitted by Wild Bottlenose Dolphins.

PubMed

Luís, Ana Rita; Couchinho, Miguel N; Dos Santos, Manuel E

2016-01-01

Common bottlenose dolphins (Tursiops truncatus), produce a wide variety of vocal emissions for communication and echolocation, of which the pulsed repertoire has been the most difficult to categorize. Packets of high repetition, broadband pulses are still largely reported under a general designation of burst-pulses, and traditional attempts to classify these emissions rely mainly in their aural characteristics and in graphical aspects of spectrograms. Here, we present a quantitative analysis of pulsed signals emitted by wild bottlenose dolphins, in the Sado estuary, Portugal (2011-2014), and test the reliability of a traditional classification approach. Acoustic parameters (minimum frequency, maximum frequency, peak frequency, duration, repetition rate and inter-click-interval) were extracted from 930 pulsed signals, previously categorized using a traditional approach. Discriminant function analysis revealed a high reliability of the traditional classification approach (93.5% of pulsed signals were consistently assigned to their aurally based categories). According to the discriminant function analysis (Wilk's Λ = 0.11, F3, 2.41 = 282.75, P < 0.001), repetition rate is the feature that best enables the discrimination of different pulsed signals (structure coefficient = 0.98). Classification using hierarchical cluster analysis led to a similar categorization pattern: two main signal types with distinct magnitudes of repetition rate were clustered into five groups. The pulsed signals, here described, present significant differences in their time-frequency features, especially repetition rate (P < 0.001), inter-click-interval (P < 0.001) and duration (P < 0.001). We document the occurrence of a distinct signal type-short burst-pulses, and highlight the existence of a diverse repertoire of pulsed vocalizations emitted in graded sequences. The use of quantitative analysis of pulsed signals is essential to improve classifications and to better assess the contexts of

A comparison of acoustic and observed sediment classifications as predictor variables for modelling biotope distributions in Galway Bay, Ireland

NASA Astrophysics Data System (ADS)

O'Carroll, Jack P. J.; Kennedy, Robert; Ren, Lei; Nash, Stephen; Hartnett, Michael; Brown, Colin

2017-10-01

The INFOMAR (Integrated Mapping For the Sustainable Development of Ireland's Marine Resource) initiative has acoustically mapped and classified a significant proportion of Ireland's Exclusive Economic Zone (EEZ), and is likely to be an important tool in Ireland's efforts to meet the criteria of the MSFD. In this study, open source and relic data were used in combination with new grab survey data to model EUNIS level 4 biotope distributions in Galway Bay, Ireland. The correct prediction rates of two artificial neural networks (ANNs) were compared to assess the effectiveness of acoustic sediment classifications versus sediments that were visually classified by an expert in the field as predictor variables. To test for autocorrelation between predictor variables the RELATE routine with Spearman rank correlation method was used. Optimal models were derived by iteratively removing predictor variables and comparing the correct prediction rates of each model. The models with the highest correct prediction rates were chosen as optimal. The optimal models each used a combination of salinity (binary; 0 = polyhaline and 1 = euhaline), proximity to reef (binary; 0 = within 50 m and 1 = outside 50 m), depth (continuous; metres) and a sediment descriptor (acoustic or observed) as predictor variables. As the status of benthic habitats is required to be assessed under the MSFD the Ecological Status (ES) of the subtidal sediments of Galway Bay was also assessed using the Infaunal Quality Index. The ANN that used observed sediment classes as predictor variables could correctly predict the distribution of biotopes 67% of the time, compared to 63% for the ANN using acoustic sediment classes. Acoustic sediment ANN predictions were affected by local sediment heterogeneity, and the lack of a mixed sediment class. The all-round poor performance of ANNs is likely to be a result of the temporally variable and sparsely distributed data within the study area.

Acoustic Response of Underwater Munitions near a Sediment Interface: Measurement Model Comparisons and Classification Schemes

DTIC Science & Technology

2015-04-23

12 Figure 4. Pulse- compressed baseband signals for sequence 40 from TREX13 …… 13 Figure 5. SAS image for sequence 40 from TREX13...12 meshes with data …………… 28 Figure 14. FE simulations for aluminum and steel replicas of an 100-mm UXO …… 28 Figure 15. FE meshes for two targets...PCB Pulse- compressed and baseband PC SWAT Personal Computer Shallow Water Acoustic Toolset PondEx09 Pond Experiment 2009 PondEx10 Pond Experiment

Acoustic communication in insect disease vectors

PubMed Central

Vigoder, Felipe de Mello; Ritchie, Michael Gordon; Gibson, Gabriella; Peixoto, Alexandre Afranio

2013-01-01

Acoustic signalling has been extensively studied in insect species, which has led to a better understanding of sexual communication, sexual selection and modes of speciation. The significance of acoustic signals for a blood-sucking insect was first reported in the XIX century by Christopher Johnston, studying the hearing organs of mosquitoes, but has received relatively little attention in other disease vectors until recently. Acoustic signals are often associated with mating behaviour and sexual selection and changes in signalling can lead to rapid evolutionary divergence and may ultimately contribute to the process of speciation. Songs can also have implications for the success of novel methods of disease control such as determining the mating competitiveness of modified insects used for mass-release control programs. Species-specific sound “signatures” may help identify incipient species within species complexes that may be of epidemiological significance, e.g. of higher vectorial capacity, thereby enabling the application of more focussed control measures to optimise the reduction of pathogen transmission. Although the study of acoustic communication in insect vectors has been relatively limited, this review of research demonstrates their value as models for understanding both the functional and evolutionary significance of acoustic communication in insects. PMID:24473800

Air-coupled acoustic thermography for in-situ evaluation

NASA Technical Reports Server (NTRS)

Zalameda, Joseph N. (Inventor); Winfree, William P. (Inventor); Yost, William T. (Inventor)

2010-01-01

Acoustic thermography uses a housing configured for thermal, acoustic and infrared radiation shielding. For in-situ applications, the housing has an open side adapted to be sealingly coupled to a surface region of a structure such that an enclosed chamber filled with air is defined. One or more acoustic sources are positioned to direct acoustic waves through the air in the enclosed chamber and towards the surface region. To activate and control each acoustic source, a pulsed signal is applied thereto. An infrared imager focused on the surface region detects a thermal image of the surface region. A data capture device records the thermal image in synchronicity with each pulse of the pulsed signal such that a time series of thermal images is generated. For enhanced sensitivity and/or repeatability, sound and/or vibrations at the surface region can be used in feedback control of the pulsed signal applied to the acoustic sources.

Modeling skull's acoustic attenuation and dispersion on photoacoustic signal

NASA Astrophysics Data System (ADS)

Mohammadi, L.; Behnam, H.; Nasiriavanaki, M. R.

2017-03-01

Despite the great promising results of a recent new transcranial photoacoustic brain imaging technology, it has been shown that the presence of the skull severely affects the performance of this imaging modality. In this paper, we investigate the effect of skull on generated photoacoustic signals with a mathematical model. The developed model takes into account the frequency dependence attenuation and acoustic dispersion effects occur with the wave reflection and refraction at the skull surface. Numerical simulations based on the developed model are performed for calculating the propagation of photoacoustic waves through the skull. From the simulation results, it was found that the skull-induced distortion becomes very important and the reconstructed image would be strongly distorted without correcting these effects. In this regard, it is anticipated that an accurate quantification and modeling of the skull transmission effects would ultimately allow for skull aberration correction in transcranial photoacoustic brain imaging.

Bondable Fluoropolymer Film as a Water Block/Acoustic Window for Environmentally Isolating Acoustic Devices

DTIC Science & Technology

2005-11-30

titanium 2 I foil which is wrapped around a polyurethane molded acoustic 2 device and local preamplifier for the purpose of water-proofing 3 and...typical sonar signals transmitted through the 14 films 12 and 14. Therefore, the film 12 can be added to 15 traditional acoustic window designs to...4 virtually no effect on typical sonar signals yet eliminates an 5 otherwise costly and relatively thick rubber or polymer skin that 6 can be

Classification of communication signals of the little brown bat

NASA Astrophysics Data System (ADS)

Melendez, Karla V.; Jones, Douglas L.; Feng, Albert S.

2005-09-01

Little brown bats, Myotis lucifugus, are known for their ability to echolocate and utilize their echolocation system to navigate, locate, and identify prey. Their echolocation signals have been characterized in detail, but their communication signals are poorly understood despite their widespread use during the social interactions. The goal of this study was to characterize the communication signals of little brown bats. Sound recordings were made overnight on five individual bats (housed separately from a large group of captive bats) for 7 nights, using a Pettersson ultrasound detector D240x bat detector and Nagra ARES-BB digital recorder. The spectral and temporal characteristics of recorded sounds were first analyzed using BATSOUND software from Pettersson. Sounds were first classified by visual observation of calls' temporal pattern and spectral composition, and later using an automatic classification scheme based on multivariate statistical parameters in MATLAB. Human- and machine-based analysis revealed five discrete classes of bat's communication signals: downward frequency-modulated calls, constant frequency calls, broadband noise bursts, broadband chirps, and broadband click trains. Future studies will focus on analysis of calls' spectrotemporal modulations to discriminate any subclasses that may exist. [Research supported by Grant R01-DC-04998 from the National Institute for Deafness and Communication Disorders.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring.

PubMed

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-05-21

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring

PubMed Central

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-01-01

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures. PMID:29883411

Acoustic signals of baby black caimans.

PubMed

Vergne, Amélie L; Aubin, Thierry; Taylor, Peter; Mathevon, Nicolas

2011-12-01

In spite of the importance of crocodilian vocalizations for the understanding of the evolution of sound communication in Archosauria and due to the small number of experimental investigations, information concerning the vocal world of crocodilians is limited. By studying black caimans Melanosuchus niger in their natural habitat, here we supply the experimental evidence that juvenile crocodilians can use a graded sound system in order to elicit adapted behavioral responses from their mother and siblings. By analyzing the acoustic structure of calls emitted in two different situations ('undisturbed context', during which spontaneous calls of juvenile caimans were recorded without perturbing the group, and a simulated 'predator attack', during which calls were recorded while shaking juveniles) and by testing their biological relevance through playback experiments, we reveal the existence of two functionally different types of juvenile calls that produce a different response from the mother and other siblings. Young black caimans can thus modulate the structure of their vocalizations along an acoustic continuum as a function of the emission context. Playback experiments show that both mother and juveniles discriminate between these 'distress' and 'contact' calls. Acoustic communication is thus an important component mediating relationships within family groups in caimans as it is in birds, their archosaurian relatives. Although probably limited, the vocal repertoire of young crocodilians is capable of transmitting the information necessary for allowing siblings and mother to modulate their behavior. Copyright © 2011 Elsevier GmbH. All rights reserved.

Bat detective-Deep learning tools for bat acoustic signal detection.

PubMed

Mac Aodha, Oisin; Gibb, Rory; Barlow, Kate E; Browning, Ella; Firman, Michael; Freeman, Robin; Harder, Briana; Kinsey, Libby; Mead, Gary R; Newson, Stuart E; Pandourski, Ivan; Parsons, Stuart; Russ, Jon; Szodoray-Paradi, Abigel; Szodoray-Paradi, Farkas; Tilova, Elena; Girolami, Mark; Brostow, Gabriel; Jones, Kate E

2018-03-01

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.

Bat detective—Deep learning tools for bat acoustic signal detection

PubMed Central

Barlow, Kate E.; Firman, Michael; Freeman, Robin; Harder, Briana; Kinsey, Libby; Mead, Gary R.; Newson, Stuart E.; Pandourski, Ivan; Russ, Jon; Szodoray-Paradi, Abigel; Tilova, Elena; Girolami, Mark; Jones, Kate E.

2018-01-01

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio. PMID:29518076

Simultaneous Detection and Classification of Acoustic Emissions in Integrated Diagnostics with Yield in Aluminum

NASA Astrophysics Data System (ADS)

Parmar, Devendra

2006-04-01

Acoustic emission (AE) experiments were conducted on a strained aluminum (10 cm x 9 cm x 0.25 cm) specimen. Studies were conducted with the goal to characterize AE associated with material yield developed due to high loading and to correlate the course of the yield with AE signals. The American Association of State Highway and Transport Officials (AASHTO) listed aluminum as one of the structural components of highway brides^1 with unit weight of 2800 kg.m-3. The specimen, mounted on the load frame, was held on each end by the wedge grips and was electromechanically tested in a tension mode at rates of extension of 0.0333 mm/s and 0.0666 mm/s. Load was applied to the test frame via moving cross heads. A load transducer (load cell) mounted in series with the specimen measured the applied load by converting it into an electrical signal. Results are analyzed using defect zone model in which location of the defect is determined from the measurement of the arrival time of the signal at two different sensors placed at strategically around the source of emission from the test object. The sensor that detects the signal first is identified to be in the defect zone. ^1AASHTO LRFD Bridge Design Specifications, 1994.

Detection of Delamination in Concrete Bridge Decks Using Mfcc of Acoustic Impact Signals

NASA Astrophysics Data System (ADS)

Zhang, G.; Harichandran, R. S.; Ramuhalli, P.

2010-02-01

Delamination of the concrete cover is a commonly observed damage in concrete bridge decks. The delamination is typically initiated by corrosion of the upper reinforcing bars and promoted by freeze-thaw cycling and traffic loading. The detection of delamination is important for bridge maintenance and acoustic non-destructive evaluation (NDE) is widely used due to its low cost, speed, and easy implementation. In traditional acoustic approaches, the inspector sounds the surface of the deck by impacting it with a hammer or bar, or by dragging a chain, and assesses delamination by the "hollowness" of the sound. The detection of the delamination is subjective and requires extensive training. To improve performance, this paper proposes an objective method for delamination detection. In this method, mel-frequency cepstral coefficients (MFCC) of the signal are extracted. Some MFCC are then selected as features for detection purposes using a mutual information criterion. Finally, the selected features are used to train a classifier which is subsequently used for detection. In this work, a simple quadratic Bayesian classifier is used. Different numbers of features are used to compare the performance of the detection method. The results show that the performance first increases with the number of features, but then decreases after an optimal value. The optimal number of features based on the recorded signals is four, and the mean error rate is only 3.3% when four features are used. Therefore, the proposed algorithm has sufficient accuracy to be used in field detection.

Wideband acoustic microscopy of tissue.

PubMed

Daft, C W; Briggs, G D

1989-01-01

A scanning acoustic microscope (SAM) has been used to measure the elastic properties of tissue with a resolution of around 8 mum. This is achieved by broadband excitation of the acoustic lens, and the recording of an undemodulated returning signal. A method of analyzing this information to yield sound velocity, acoustic impedance, section thickness, and acoustic attenuation is described. Results from a sample of skin tissue are presented and compared with data from a computer simulation of the experiment.

Ultrasound-modulated optical tomography with intense acoustic bursts.

PubMed

Zemp, Roger J; Kim, Chulhong; Wang, Lihong V

2007-04-01

Ultrasound-modulated optical tomography (UOT) detects ultrasonically modulated light to spatially localize multiply scattered photons in turbid media with the ultimate goal of imaging the optical properties in living subjects. A principal challenge of the technique is weak modulated signal strength. We discuss ways to push the limits of signal enhancement with intense acoustic bursts while conforming to optical and ultrasonic safety standards. A CCD-based speckle-contrast detection scheme is used to detect acoustically modulated light by measuring changes in speckle statistics between ultrasound-on and ultrasound-off states. The CCD image capture is synchronized with the ultrasound burst pulse sequence. Transient acoustic radiation force, a consequence of bursts, is seen to produce slight signal enhancement over pure ultrasonic-modulation mechanisms for bursts and CCD exposure times of the order of milliseconds. However, acoustic radiation-force-induced shear waves are launched away from the acoustic sample volume, which degrade UOT spatial resolution. By time gating the CCD camera to capture modulated light before radiation force has an opportunity to accumulate significant tissue displacement, we reduce the effects of shear-wave image degradation, while enabling very high signal-to-noise ratios. Additionally, we maintain high-resolution images representative of optical and not mechanical contrast. Signal-to-noise levels are sufficiently high so as to enable acquisition of 2D images of phantoms with one acoustic burst per pixel.

Sound Classification in Hearing Aids Inspired by Auditory Scene Analysis

NASA Astrophysics Data System (ADS)

Büchler, Michael; Allegro, Silvia; Launer, Stefan; Dillier, Norbert

2005-12-01

A sound classification system for the automatic recognition of the acoustic environment in a hearing aid is discussed. The system distinguishes the four sound classes "clean speech," "speech in noise," "noise," and "music." A number of features that are inspired by auditory scene analysis are extracted from the sound signal. These features describe amplitude modulations, spectral profile, harmonicity, amplitude onsets, and rhythm. They are evaluated together with different pattern classifiers. Simple classifiers, such as rule-based and minimum-distance classifiers, are compared with more complex approaches, such as Bayes classifier, neural network, and hidden Markov model. Sounds from a large database are employed for both training and testing of the system. The achieved recognition rates are very high except for the class "speech in noise." Problems arise in the classification of compressed pop music, strongly reverberated speech, and tonal or fluctuating noises.

The broadband social acoustic signaling behavior of spinner and spotted dolphins

NASA Astrophysics Data System (ADS)

Lammers, Marc O.; Au, Whitlow W. L.; Herzing, Denise L.

2003-09-01

Efforts to study the social acoustic signaling behavior of delphinids have traditionally been restricted to audio-range (<20 kHz) analyses. To explore the occurrence of communication signals at ultrasonic frequencies, broadband recordings of whistles and burst pulses were obtained from two commonly studied species of delphinids, the Hawaiian spinner dolphin (Stenella longirostris) and the Atlantic spotted dolphin (Stenella frontalis). Signals were quantitatively analyzed to establish their full bandwidth, to identify distinguishing characteristics between each species, and to determine how often they occur beyond the range of human hearing. Fundamental whistle contours were found to extend beyond 20 kHz only rarely among spotted dolphins, but with some regularity in spinner dolphins. Harmonics were present in the majority of whistles and varied considerably in their number, occurrence, and amplitude. Many whistles had harmonics that extended past 50 kHz and some reached as high as 100 kHz. The relative amplitude of harmonics and the high hearing sensitivity of dolphins to equivalent frequencies suggest that harmonics are biologically relevant spectral features. The burst pulses of both species were found to be predominantly ultrasonic, often with little or no energy below 20 kHz. The findings presented reveal that the social signals produced by spinner and spotted dolphins span the full range of their hearing sensitivity, are spectrally quite varied, and in the case of burst pulses are probably produced more frequently than reported by audio-range analyses.

The broadband social acoustic signaling behavior of spinner and spotted dolphins.

PubMed

Lammers, Marc O; Au, Whitlow W L; Herzing, Denise L

2003-09-01

Efforts to study the social acoustic signaling behavior of delphinids have traditionally been restricted to audio-range (<20 kHz) analyses. To explore the occurrence of communication signals at ultrasonic frequencies, broadband recordings of whistles and burst pulses were obtained from two commonly studied species of delphinids, the Hawaiian spinner dolphin (Stenella longirostris) and the Atlantic spotted dolphin (Stenella frontalis). Signals were quantitatively analyzed to establish their full bandwidth, to identify distinguishing characteristics between each species, and to determine how often they occur beyond the range of human hearing. Fundamental whistle contours were found to extend beyond 20 kHz only rarely among spotted dolphins, but with some regularity in spinner dolphins. Harmonics were present in the majority of whistles and varied considerably in their number, occurrence, and amplitude. Many whistles had harmonics that extended past 50 kHz and some reached as high as 100 kHz. The relative amplitude of harmonics and the high hearing sensitivity of dolphins to equivalent frequencies suggest that harmonics are biologically relevant spectral features. The burst pulses of both species were found to be predominantly ultrasonic, often with little or no energy below 20 kHz. The findings presented reveal that the social signals produced by spinner and spotted dolphins span the full range of their hearing sensitivity, are spectrally quite varied, and in the case of burst pulses are probably produced more frequently than reported by audio-range analyses.

Magneto acoustic emission apparatus for testing materials for embrittlement

NASA Technical Reports Server (NTRS)

Allison, Sidney G. (Inventor); Min, Namkung (Inventor); Yost, William T. (Inventor); Cantrell, John H. (Inventor)

1990-01-01

A method and apparatus for testing steel components for temper embrittlement uses magneto-acoustic emission to nondestructively evaluate the component. Acoustic emission signals occur more frequently at higher levels in embrittled components. A pair of electromagnets are used to create magnetic induction in the test component. Magneto-acoustic emission signals may be generated by applying an ac current to the electromagnets. The acoustic emission signals are analyzed to provide a comparison between a component known to be unembrittled and a test component. Magnetic remanence is determined by applying a dc current to the electromagnets, then turning the magnets off and observing the residual magnetic induction.

Acoustic signal propagation and measurement in natural stream channels for application to surrogate bed load measurements: Halfmoon Creek, Colorado

USDA-ARS?s Scientific Manuscript database

Monitoring sediment-generated noise using submerged hydrophones is a surrogate method for measuring bed load transport in streams with the potential for improving estimates of bed load transport through widespread, inexpensive monitoring. Understanding acoustic signal propagation in natural stream e...

Acoustic velocity meter systems

USGS Publications Warehouse

Laenen, Antonius

1985-01-01

Acoustic velocity meter (AVM) systems operate on the principles that the point-to-point upstream traveltime of an acoustic pulse is longer than the downstream traveltime and that this difference in traveltime can be accurately measured by electronic devices. An AVM system is capable of recording water velocity (and discharge) under a wide range of conditions, but some constraints apply: 1. Accuracy is reduced and performance is degraded if the acoustic path is not a continuous straight line. The path can be bent by reflection if it is too close to a stream boundary or by refraction if it passes through density gradients resulting from variations in either water temperature or salinity. For paths of less than 100 m, a temperature gradient of 0.1' per meter causes signal bending less than 0.6 meter at midchannel, and satisfactory velocity results can be obtained. Reflection from stream boundaries can cause signal cancellation if boundaries are too close to signal path. 2. Signal strength is attenuated by particles or bubbles that absorb, spread, or scatter sound. The concentration of particles or bubbles that can be tolerated is a function of the path length and frequency of the acoustic signal. 3. Changes in streamline orientation can affect system accuracy if the variability is random. 4. Errors relating to signal resolution are much larger for a single threshold detection scheme than for multiple threshold schemes. This report provides methods for computing the effect of various conditions on the accuracy of a record obtained from an AVM. The equipment must be adapted to the site. Field reconnaissance and preinstallation analysis to detect possible problems are critical for proper installation and operation of an AVM system.

New early warning system for gravity-driven ruptures based on codetection of acoustic signal

NASA Astrophysics Data System (ADS)

Faillettaz, J.

2016-12-01

Gravity-driven rupture phenomena in natural media - e.g. landslide, rockfalls, snow or ice avalanches - represent an important class of natural hazards in mountainous regions. To protect the population against such events, a timely evacuation often constitutes the only effective way to secure the potentially endangered area. However, reliable prediction of imminence of such failure events remains challenging due to the nonlinear and complex nature of geological material failure hampered by inherent heterogeneity, unknown initial mechanical state, and complex load application (rainfall, temperature, etc.). Here, a simple method for real-time early warning that considers both the heterogeneity of natural media and characteristics of acoustic emissions attenuation is proposed. This new method capitalizes on codetection of elastic waves emanating from microcracks by multiple and spatially separated sensors. Event-codetection is considered as surrogate for large event size with more frequent codetected events (i.e., detected concurrently on more than one sensor) marking imminence of catastrophic failure. Simple numerical model based on a Fiber Bundle Model considering signal attenuation and hypothetical arrays of sensors confirms the early warning potential of codetection principles. Results suggest that although statistical properties of attenuated signal amplitude could lead to misleading results, monitoring the emergence of large events announcing impeding failure is possible even with attenuated signals depending on sensor network geometry and detection threshold. Preliminary application of the proposed method to acoustic emissions during failure of snow samples has confirmed the potential use of codetection as indicator for imminent failure at lab scale. The applicability of such simple and cheap early warning system is now investigated at a larger scale (hillslope). First results of such a pilot field experiment are presented and analysed.

Auditory object salience: human cortical processing of non-biological action sounds and their acoustic signal attributes

PubMed Central

Lewis, James W.; Talkington, William J.; Tallaksen, Katherine C.; Frum, Chris A.

2012-01-01

Whether viewed or heard, an object in action can be segmented as a distinct salient event based on a number of different sensory cues. In the visual system, several low-level attributes of an image are processed along parallel hierarchies, involving intermediate stages wherein gross-level object form and/or motion features are extracted prior to stages that show greater specificity for different object categories (e.g., people, buildings, or tools). In the auditory system, though relying on a rather different set of low-level signal attributes, meaningful real-world acoustic events and “auditory objects” can also be readily distinguished from background scenes. However, the nature of the acoustic signal attributes or gross-level perceptual features that may be explicitly processed along intermediate cortical processing stages remain poorly understood. Examining mechanical and environmental action sounds, representing two distinct non-biological categories of action sources, we had participants assess the degree to which each sound was perceived as object-like versus scene-like. We re-analyzed data from two of our earlier functional magnetic resonance imaging (fMRI) task paradigms (Engel et al., 2009) and found that scene-like action sounds preferentially led to activation along several midline cortical structures, but with strong dependence on listening task demands. In contrast, bilateral foci along the superior temporal gyri (STG) showed parametrically increasing activation to action sounds rated as more “object-like,” independent of sound category or task demands. Moreover, these STG regions also showed parametric sensitivity to spectral structure variations (SSVs) of the action sounds—a quantitative measure of change in entropy of the acoustic signals over time—and the right STG additionally showed parametric sensitivity to measures of mean entropy and harmonic content of the environmental sounds. Analogous to the visual system, intermediate stages

Novel Fiber-Optic Ring Acoustic Emission Sensor.

PubMed

Wei, Peng; Han, Xiaole; Xia, Dong; Liu, Taolin; Lang, Hao

2018-01-13

Acoustic emission technology has been applied to many fields for many years. However, the conventional piezoelectric acoustic emission sensors cannot be used in extreme environments, such as those with heavy electromagnetic interference, high pressure, or strong corrosion. In this paper, a novel fiber-optic ring acoustic emission sensor is proposed. The sensor exhibits high sensitivity, anti-electromagnetic interference, and corrosion resistance. First, the principle of a novel fiber-optic ring sensor is introduced. Different from piezoelectric and other fiber acoustic emission sensors, this novel sensor includes both a sensing skeleton and a sensing fiber. Second, a heterodyne interferometric demodulating method is presented. In addition, a fiber-optic ring sensor acoustic emission system is built based on this method. Finally, fiber-optic ring acoustic emission experiments are performed. The novel fiber-optic ring sensor is glued onto the surface of an aluminum plate. The 150 kHz standard continuous sinusoidal signals and broken lead signals are successfully detected by the novel fiber-optic ring acoustic emission sensor. In addition, comparison to the piezoelectric acoustic emission sensor is performed, which shows the availability and reliability of the novel fiber-optic ring acoustic emission sensor. In the future, this novel fiber-optic ring acoustic emission sensor will provide a new route to acoustic emission detection in harsh environments.

Exploring the feasibility of smart phone microphone for measurement of acoustic voice parameters and voice pathology screening.

PubMed

Uloza, Virgilijus; Padervinskis, Evaldas; Vegiene, Aurelija; Pribuisiene, Ruta; Saferis, Viktoras; Vaiciukynas, Evaldas; Gelzinis, Adas; Verikas, Antanas

2015-11-01

The objective of this study is to evaluate the reliability of acoustic voice parameters obtained using smart phone (SP) microphones and investigate the utility of use of SP voice recordings for voice screening. Voice samples of sustained vowel/a/obtained from 118 subjects (34 normal and 84 pathological voices) were recorded simultaneously through two microphones: oral AKG Perception 220 microphone and SP Samsung Galaxy Note3 microphone. Acoustic voice signal data were measured for fundamental frequency, jitter and shimmer, normalized noise energy (NNE), signal to noise ratio and harmonic to noise ratio using Dr. Speech software. Discriminant analysis-based Correct Classification Rate (CCR) and Random Forest Classifier (RFC) based Equal Error Rate (EER) were used to evaluate the feasibility of acoustic voice parameters classifying normal and pathological voice classes. Lithuanian version of Glottal Function Index (LT_GFI) questionnaire was utilized for self-assessment of the severity of voice disorder. The correlations of acoustic voice parameters obtained with two types of microphones were statistically significant and strong (r = 0.73-1.0) for the entire measurements. When classifying into normal/pathological voice classes, the Oral-NNE revealed the CCR of 73.7% and the pair of SP-NNE and SP-shimmer parameters revealed CCR of 79.5%. However, fusion of the results obtained from SP voice recordings and GFI data provided the CCR of 84.60% and RFC revealed the EER of 7.9%, respectively. In conclusion, measurements of acoustic voice parameters using SP microphone were shown to be reliable in clinical settings demonstrating high CCR and low EER when distinguishing normal and pathological voice classes, and validated the suitability of the SP microphone signal for the task of automatic voice analysis and screening.

Acoustic Scattering Classification of Zooplankton and Microstructure

DTIC Science & Technology

2002-09-30

the scattering in different areas. In some cases, siphonophores dominated the scattering; in other cases, euphausiids were the dominant scatterers...juvenile form of siphonophores ) through the use of BIOMAPER-II acoustics and video systems. Because of their fragility, these organisms are...scattering strength, total biomass, siphonophore abundance, and water temperature, throughout the water column in a one-hour section of a transect

Acoustic constituents of prosodic typology

NASA Astrophysics Data System (ADS)

Komatsu, Masahiko

Different languages sound different, and considerable part of it derives from the typological difference of prosody. Although such difference is often referred to as lexical accent types (stress accent, pitch accent, and tone; e.g. English, Japanese, and Chinese respectively) and rhythm types (stress-, syllable-, and mora-timed rhythms; e.g. English, Spanish, and Japanese respectively), it is unclear whether these types are determined in terms of acoustic properties, The thesis intends to provide a potential basis for the description of prosody in terms of acoustics. It argues for the hypothesis that the source component of the source-filter model (acoustic features) approximately corresponds to prosody (linguistic features) through several experimental-phonetic studies. The study consists of four parts. (1) Preliminary experiment: Perceptual language identification tests were performed using English and Japanese speech samples whose frequency spectral information (i.e. non-source component) is heavily reduced. The results indicated that humans can discriminate languages with such signals. (2) Discussion on the linguistic information that the source component contains: This part constitutes the foundation of the argument of the thesis. Perception tests of consonants with the source signal indicated that the source component carries the information on broad categories of phonemes that contributes to the creation of rhythm. (3) Acoustic analysis: The speech samples of Chinese, English, Japanese, and Spanish, differing in prosodic types, were analyzed. These languages showed difference in acoustic characteristics of the source component. (4) Perceptual experiment: A language identification test for the above four languages was performed using the source signal with its acoustic features parameterized. It revealed that humans can discriminate prosodic types solely with the source features and that the discrimination is easier as acoustic information increases. The

Geo-Acoustic Doppler Spectroscopy: A Novel Acoustic Technique For Surveying The Seabed

NASA Astrophysics Data System (ADS)

Buckingham, Michael J.

2010-09-01

An acoustic inversion technique, known as Geo-Acoustic Doppler Spectroscopy, has recently been developed for estimating the geo-acoustic parameters of the seabed in shallow water. The technique is unusual in that it utilizes a low-flying, propeller-driven light aircraft as an acoustic source. Both the engine and propeller produce sound and, since they are rotating sources, the acoustic signature of each takes the form of a sequence of narrow-band harmonics. Although the coupling of the harmonics across the air-sea interface is inefficient, due to the large impedance mismatch between air and water, sufficient energy penetrates the sea surface to provide a useable underwater signal at sensors either in the water column or buried in the sediment. The received signals, which are significantly Doppler shifted due to the motion of the aircraft, will have experienced a number of reflections from the seabed and thus they contain information about the sediment. A geo-acoustic inversion of the Doppler-shifted modes associated with each harmonic yields an estimate of the sound speed in the sediment; and, once the sound speed has been determined, the known correlations between it and the remaining geo-acoustic parameters allow all of the latter to be computed. This inversion technique has been applied to aircraft data collected in the shallow water north of Scripps pier, returning values of the sound speed, shear speed, porosity, density and grain size that are consistent with the known properties of the sandy sediment in the channel.

Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals

NASA Astrophysics Data System (ADS)

Bai, F.; Gagar, D.; Foote, P.; Zhao, Y.

2017-02-01

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors in an array is essential in performing localisation. Currently, this is determined using a fixed threshold which is particularly prone to errors when not set to optimal values. This paper presents three new methods for determining the onset of AE signals without the need for a predetermined threshold. The performance of the techniques is evaluated using AE signals generated during fatigue crack growth and compared to the established Akaike Information Criterion (AIC) and fixed threshold methods. It was found that the 1D location accuracy of the new methods was within the range of < 1 - 7.1 % of the monitored region compared to 2.7% for the AIC method and a range of 1.8-9.4% for the conventional Fixed Threshold method at different threshold levels.

Method for simultaneously making a plurality of acoustic signal sensor elements

NASA Technical Reports Server (NTRS)

Bryant, Timothy D. (Inventor); Wynkoop, Mark W. (Inventor); Holloway, Nancy M. H. (Inventor); Zuckerwar, Allan J. (Inventor)

2005-01-01

A fetal heart monitoring system preferably comprising a backing plate having a generally concave front surface and a generally convex back surface, and at least one sensor element attached to the concave front surface for acquiring acoustic fetal heart signals produced by a fetus within a body. The sensor element has a shape that conforms to the generally concave back surface of the backing plate. In one embodiment, the at least one sensor element comprises an inner sensor, and a plurality of outer sensors surrounding the inner sensor. The fetal heart monitoring system can further comprise a web belt, and a web belt guide movably attached to the web belt. The web belt guide being is to the convex back surface of the backing plate.

Method for Simultaneously Making a Plurality of Acoustic Signal Sensor Elements

NASA Technical Reports Server (NTRS)

Bryant, Timothy D.; Wynkoop, Mark W.; Holloway, Nancy M. H.; Zuckerwar, Allan J.

2005-01-01

A fetal heart monitoring system preferably comprising a backing plate having a generally concave front surface and a generally convex back surface, and at least one sensor element attached to the concave front surface for acquiring acoustic fetal heart signals produced by a fetus within a body. The sensor element has a shape that conforms to the generally concave back surface of the backing plate. In one embodiment, the at least one sensor element comprises an inner sensor, and a plurality of outer sensors surrounding the inner sensor. The fetal heart monitoring system can further comprise a web belt, and a web belt guide movably attached to the web belt. The web belt guide being is to the convex back surface of the backing plate.

An artificial EMG generation model based on signal-dependent noise and related application to motion classification

PubMed Central

Hayashi, Hideaki; Nakamura, Go; Chin, Takaaki; Tsuji, Toshio

2017-01-01

This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance. This facilitates representation of artificial EMG signals with signal-dependent noise superimposed according to the muscle activation levels. The frequency characteristics of the EMG signals are also simulated via a shaping filter with parameters determined by an autoregressive model. An estimation method to determine EMG variance distribution using rectified and smoothed EMG signals, thereby allowing model parameter estimation with a small number of samples, is also incorporated in the proposed model. Moreover, the prediction of variance distribution with strong muscle contraction from EMG signals with low muscle contraction and related artificial EMG generation are also described. The results of experiments conducted, in which the reproduction capability of the proposed model was evaluated through comparison with measured EMG signals in terms of amplitude, frequency content, and EMG distribution demonstrate that the proposed model can reproduce the features of measured EMG signals. Further, utilizing the generated EMG signals as training data for a neural network resulted in the classification of upper limb motion with a higher precision than by learning from only measured EMG signals. This indicates that the proposed model is also applicable to motion classification. PMID:28640883

Analysis of Traffic Signals on a Software-Defined Network for Detection and Classification of a Man-in-the-Middle Attack

DTIC Science & Technology

2017-09-01

unique characteristics of reported anomalies in the collected traffic signals to build a classification framework. Other cyber events, such as a...Furthermore, we identify unique characteristics of reported anomalies in the collected traffic signals to build a classification framework. Other cyber...2]. The applications build flow rules using network topology information provided by the control plane [1]. Since the control plane is able to

West Texas array experiment: Noise and source characterization of short-range infrasound and acoustic signals, along with lab and field evaluation of Intermountain Laboratories infrasound microphones

NASA Astrophysics Data System (ADS)

Fisher, Aileen

spatial wind noise filtering hoses or pipes. The grid was within the distance limits of a single gauge's normal hose array, and data were used to perform a spatial noise correlation study. The highest correlation values were not found in the lower frequencies as anticipated, owing to a lack of sources in the lower range and the uncorrelated nature of wind noise. The highest values, with cross-correlation averages between 0.4 and 0.7 from 3 to 17 m between gauges, were found at night from 10 and 20 Hz due to a continuous local noise source and low wind. Data from the larger array were used to identify continuous and impulsive signals in the area that comprise the ambient noise field. Ground truth infrasound and acoustic, time and location data were taken for a highway site, a wind farm, and a natural gas compressor. Close-range sound data were taken with a single IML "traveler" gauge. Spectrograms and spectrum peaks were used to identify their source signatures. Two regional location techniques were also tested with data from the large array by using a propane cannon as a controlled, impulsive source. A comparison is presented of the Multiple Signal Classification Algorithm (MUSIC) to a simple, quadratic, circular wavefront algorithm. MUSIC was unable to effectively separate noise and source eignenvalues and eigenvectors due to spatial aliasing of the propane cannon signal and a lack of incoherent noise. Only 33 out of 80 usable shots were located by MUSIC within 100 m. Future work with the algorithm should focus on location of impulsive and continuous signals with development of methods for accurate separation of signal and noise eigenvectors in the presence of coherent noise and possible spatial aliasing. The circular wavefront algorithm performed better with our specific dataset and successfully located 70 out of 80 propane cannon shots within 100 m of the original location, 66 of which were within 20 m. This method has low computation requirements, making it well

Identification and classification of failure modes in laminated composites by using a multivariate statistical analysis of wavelet coefficients

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2017-11-01

Acoustic Emission (AE) is a suitable method to monitor the health of composite structures in real-time. However, AE-based failure mode identification and classification are still complex to apply due to the fact that AE waves are generally released simultaneously from all AE-emitting damage sources. Hence, the use of advanced signal processing techniques in combination with pattern recognition approaches is required. In this paper, AE signals generated from laminated carbon fiber reinforced polymer (CFRP) subjected to indentation test are examined and analyzed. A new pattern recognition approach involving a number of processing steps able to be implemented in real-time is developed. Unlike common classification approaches, here only CWT coefficients are extracted as relevant features. Firstly, Continuous Wavelet Transform (CWT) is applied to the AE signals. Furthermore, dimensionality reduction process using Principal Component Analysis (PCA) is carried out on the coefficient matrices. The PCA-based feature distribution is analyzed using Kernel Density Estimation (KDE) allowing the determination of a specific pattern for each fault-specific AE signal. Moreover, waveform and frequency content of AE signals are in depth examined and compared with fundamental assumptions reported in this field. A correlation between the identified patterns and failure modes is achieved. The introduced method improves the damage classification and can be used as a non-destructive evaluation tool.

Novel Fiber-Optic Ring Acoustic Emission Sensor

PubMed Central

Han, Xiaole; Xia, Dong; Liu, Taolin; Lang, Hao

2018-01-01

Acoustic emission technology has been applied to many fields for many years. However, the conventional piezoelectric acoustic emission sensors cannot be used in extreme environments, such as those with heavy electromagnetic interference, high pressure, or strong corrosion. In this paper, a novel fiber-optic ring acoustic emission sensor is proposed. The sensor exhibits high sensitivity, anti-electromagnetic interference, and corrosion resistance. First, the principle of a novel fiber-optic ring sensor is introduced. Different from piezoelectric and other fiber acoustic emission sensors, this novel sensor includes both a sensing skeleton and a sensing fiber. Second, a heterodyne interferometric demodulating method is presented. In addition, a fiber-optic ring sensor acoustic emission system is built based on this method. Finally, fiber-optic ring acoustic emission experiments are performed. The novel fiber-optic ring sensor is glued onto the surface of an aluminum plate. The 150 kHz standard continuous sinusoidal signals and broken lead signals are successfully detected by the novel fiber-optic ring acoustic emission sensor. In addition, comparison to the piezoelectric acoustic emission sensor is performed, which shows the availability and reliability of the novel fiber-optic ring acoustic emission sensor. In the future, this novel fiber-optic ring acoustic emission sensor will provide a new route to acoustic emission detection in harsh environments. PMID:29342858

Transition section for acoustic waveguides

DOEpatents

Karplus, H.H.B.

1975-10-28

A means of facilitating the transmission of acoustic waves with minimal reflection between two regions having different specific acoustic impedances is described comprising a region exhibiting a constant product of cross-sectional area and specific acoustic impedance at each cross-sectional plane along the axis of the transition region. A variety of structures that exhibit this feature is disclosed, the preferred embodiment comprising a nested structure of doubly reentrant cones. This structure is useful for monitoring the operation of nuclear reactors in which random acoustic signals are generated in the course of operation.

Laser-induced acoustic imaging of underground objects

NASA Astrophysics Data System (ADS)

Li, Wen; DiMarzio, Charles A.; McKnight, Stephen W.; Sauermann, Gerhard O.; Miller, Eric L.

1999-02-01

This paper introduces a new demining technique based on the photo-acoustic interaction, together with results from photo- acoustic experiments. We have buried different types of targets (metal, rubber and plastic) in different media (sand, soil and water) and imaged them by measuring reflection of acoustic waves generated by irradiation with a CO2 laser. Research has been focused on the signal acquisition and signal processing. A deconvolution method using Wiener filters is utilized in data processing. Using a uniform spatial distribution of laser pulses at the ground's surface, we obtained 3D images of buried objects. The images give us a clear representation of the shapes of the underground objects. The quality of the images depends on the mismatch of acoustic impedance of the buried objects, the bandwidth and center frequency of the acoustic sensors and the selection of filter functions.

Acoustically based fetal heart rate monitor

NASA Technical Reports Server (NTRS)

Baker, Donald A.; Zuckerwar, Allan J.

1991-01-01

The acoustically based fetal heart rate monitor permits an expectant mother to perform the fetal Non-Stress Test in her home. The potential market would include the one million U.S. pregnancies per year requiring this type of prenatal surveillance. The monitor uses polyvinylidene fluoride (PVF2) piezoelectric polymer film for the acoustic sensors, which are mounted in a seven-element array on a cummerbund. Evaluation of the sensor ouput signals utilizes a digital signal processor, which performs a linear prediction routine in real time. Clinical tests reveal that the acoustically based monitor provides Non-Stress Test records which are comparable to those obtained with a commercial ultrasonic transducer.

Periodic shock-emission from acoustically driven cavitation clouds: a source of the subharmonic signal.

PubMed

Johnston, Keith; Tapia-Siles, Cecilia; Gerold, Bjoern; Postema, Michiel; Cochran, Sandy; Cuschieri, Alfred; Prentice, Paul

2014-12-01

Single clouds of cavitation bubbles, driven by 254kHz focused ultrasound at pressure amplitudes in the range of 0.48-1.22MPa, have been observed via high-speed shadowgraphic imaging at 1×10(6) frames per second. Clouds underwent repetitive growth, oscillation and collapse (GOC) cycles, with shock-waves emitted periodically at the instant of collapse during each cycle. The frequency of cloud collapse, and coincident shock-emission, was primarily dependent on the intensity of the focused ultrasound driving the activity. The lowest peak-to-peak pressure amplitude of 0.48MPa generated shock-waves with an average period of 7.9±0.5μs, corresponding to a frequency of f0/2, half-harmonic to the fundamental driving. Increasing the intensity gave rise to GOC cycles and shock-emission periods of 11.8±0.3, 15.8±0.3, 19.8±0.2μs, at pressure amplitudes of 0.64, 0.92 and 1.22MPa, corresponding to the higher-order subharmonics of f0/3, f0/4 and f0/5, respectively. Parallel passive acoustic detection, filtered for the fundamental driving, revealed features that correlated temporally to the shock-emissions observed via high-speed imaging, p(two-tailed) < 0.01 (r=0.996, taken over all data). Subtracting the isolated acoustic shock profiles from the raw signal collected from the detector, demonstrated the removal of subharmonic spectral peaks, in the frequency domain. The larger cavitation clouds (>200μm diameter, at maximum inflation), that developed under insonations of peak-to-peak pressure amplitudes >1.0MPa, emitted shock-waves with two or more fronts suggesting non-uniform collapse of the cloud. The observations indicate that periodic shock-emissions from acoustically driven cavitation clouds provide a source for the cavitation subharmonic signal, and that shock structure may be used to study intra-cloud dynamics at sub-microsecond timescales. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Long-term recording and automatic analysis of cough using filtered acoustic signals and movements on static charge sensitive bed.

PubMed

Salmi, T; Sovijärvi, A R; Brander, P; Piirilä, P

1988-11-01

Reliable long-term assessment of cough is necessary in many clinical and scientific settings. A new method for long-term recording and automatic analysis of cough is presented. The method is based on simultaneous recording of two independent signals: high-pass filtered cough sounds and cough-induced fast movements of the body. The acoustic signals are recorded with a dynamic microphone in the acoustic focus of a glass fiber paraboloid mirror. Body movements are recorded with a static charge-sensitive bed located under an ordinary plastic foam mattress. The patient can be studied lying or sitting with no transducers or electrodes attached. A microcomputer is used for sampling of signals, detection of cough, statistical analyses, and on-line printing of results. The method was validated in seven adult patients with a total of 809 spontaneous cough events, using clinical observation as a reference. The sensitivity of the method to detect cough was 99.0 percent, and the positive predictivity was 98.1 percent. The system ignored speaking and snoring. The method provides a convenient means of reliable long-term follow-up of cough in clinical work and research.

Use of Pattern Classification Algorithms to Interpret Passive and Active Data Streams from a Walking-Speed Robotic Sensor Platform

NASA Astrophysics Data System (ADS)

Dieckman, Eric Allen

In order to perform useful tasks for us, robots must have the ability to notice, recognize, and respond to objects and events in their environment. This requires the acquisition and synthesis of information from a variety of sensors. Here we investigate the performance of a number of sensor modalities in an unstructured outdoor environment, including the Microsoft Kinect, thermal infrared camera, and coffee can radar. Special attention is given to acoustic echolocation measurements of approaching vehicles, where an acoustic parametric array propagates an audible signal to the oncoming target and the Kinect microphone array records the reflected backscattered signal. Although useful information about the target is hidden inside the noisy time domain measurements, the Dynamic Wavelet Fingerprint process (DWFP) is used to create a time-frequency representation of the data. A small-dimensional feature vector is created for each measurement using an intelligent feature selection process for use in statistical pattern classification routines. Using our experimentally measured data from real vehicles at 50 m, this process is able to correctly classify vehicles into one of five classes with 94% accuracy. Fully three-dimensional simulations allow us to study the nonlinear beam propagation and interaction with real-world targets to improve classification results.

Signal characteristics of electroseismic conversion

NASA Astrophysics Data System (ADS)

Peng, Rong; Di, Bangrang; Wei, Jianxin; Ding, Pinbo; Liu, Zichun; Guan, Bingyan; Huang, Shiqi

2018-04-01

Electric fields applying on the fluid-filled porous materials can induce small relative pore-fluid motions due to electroseismic conversions. In order to characterize the electroseismic propagation phenomena, we have designed an experimental apparatus to acquire the electroseismic (ES) signals. The electroseismic measurements on different samples have been conducted to confirm the origin of the recorded signals. We find that a strong acoustic signal generates around the electrode and affects the identification of ES signals. To further confirm and distinguish the ES signal as well as the acoustic signal around the electrode, we have analyzed records obtained with regular movements of the receiver, the sample and the source. Analysis has been made on the characteristics of the traveltime, polarity and frequency of ES signals. Our results show that the traveltime of ES signal relates to the distance between the rock sample and the receiver, the location of the exciting electrode has little impact on the traveltime. The applied electric field influences the polarity of ES signal, the polarity of ES signal reverses along with the changes of the electric field direction. While it has no polarity effects on the acoustic signal generated around the electrode. The frequency spectrum of ES signal is absolutely different with that of the acoustic signal generated around the electrode. The acoustic signal around the electrode has multiple dominant frequencies which are mainly in low-frequency range without being affected by the frequency of the electric field. The ES signal has only one dominant frequency which closely relates to the frequency of the electric field. The understanding of the signal characteristics on electroseismic conversion can contribute to a better application and interpretation of ES exploration.

Acoustic cross-correlation flowmeter for solid-gas flow

DOEpatents

Sheen, S.H.; Raptis, A.C.

1984-05-14

Apparatus for measuring particle velocity in a solid-gas flow within a pipe includes: first and second transmitting transducers for transmitting first and second ultrasonic signals into the pipe at first and second locations, respectively, along the pipe; an acoustic decoupler, positioned between said first and second transmitting transducers, for acoustically isolating said first and second signals from one another; first and second detecting transducers for detecting said first and second signals and for generating first and second detected signals; and means for cross-correlating said first and second output signals.

Natural vs human-induced changes at the Tauranga Harbour area (New Zealand): a time -series acoustic seabed classification comparison

NASA Astrophysics Data System (ADS)

Capperucci, Ruggero Maria; Bartholomä, Alexander; Renken, Sabrina; De Lange, Willem

2013-04-01

The Tauranga Harbour Bay (New Zealand) is a mesotidal estuary system, enclosed by the Matakana barrier island. It hosts the leading export port in New Zealand and the second largest import port by value. Coastal changes are well documented over the last decades, mainly at the southern entrance of the area, between Matakana Island and Mt. Maunganui. It is an extremely dynamic environment, where natural processes are strongly influenced by human activities. In particular, the understanding of the recent evolution of the system is crucial for policymakers. In fact, the cumulative impact due to the maintenance of the port (mainly dredging activities, shipping, facilities construction, but also increasing tourism) and its already approved expansion clashes with the claim of the local Maori communities, which recently leaded to a court action. A hydroacoustic multiple-device survey (Side-scan Sonar SSS, Multibeam Echo-sounder MBES and Single Beam Echo-sounder) coupled with sediment sampling was carried out in March 2011 over an area of 0.8 km2, southern Matakana Island, along the Western Channel. The area is not directly impacted by dredging activities, resulting in an optimal testing site for assessing indirect effects of human disturbance on coastal dynamics. The main goals were: 1. To test the response of different acoustic systems in such a highly dynamic environment; 2. To study the influence of dredging activities on sediment dynamics and habitat changes, by means of comparing the current data with existing ones, in order to distinguish between natural and human induced changes Results demonstrate a good agreement between acoustic classifications from different systems. They seem to be mainly driven by the sediment distribution, with a distinctive fingerprint given by shells and shell fragments. Nevertheless, the presence of relevant topographic features (i.e. large bedform fields) influences swath-looking systems (SSS and MBES). SSS and MBES classifications tend

Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

PubMed

Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

2016-01-01

In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

Acoustic cross-correlation flowmeter for solid-gas flow

DOEpatents

Sheen, Shuh-Haw; Raptis, Apostolos C.

1986-01-01

Apparatus for measuring particle velocity in a solid-gas flow within a pipe includes: first and second transmitting transducers for transmitting first and second ultrasonic signals into the pipe at first and second locations, respectively, along the pipe; an acoustic decoupler, positioned between said first and second transmitting transducers, for acoustically isolating said first and second signals from one another; first and second detecting transducers for detecting said first and second signals and for generating first and second detected signals in response to said first and second detected signals; and means for cross-correlating said first and second output signals.

Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes

NASA Astrophysics Data System (ADS)

Morozov, Yu. V.; Spektor, A. A.

2017-11-01

A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

How many rumbles are there? Acoustic variation and individual identity in the rumble vocalizations of African elephants (Loxodonta africana)

NASA Astrophysics Data System (ADS)

Soltis, Joseph M.; Savage, Anne; Leong, Kirsten M.

2004-05-01

The most commonly occurring elephant vocalization is the rumble, a frequency-modulated call with infrasonic components. Upwards of ten distinct rumble subtypes have been proposed, but little quantitative work on the acoustic properties of rumbles has been conducted. Rumble vocalizations (N=269) from six females housed at Disney's Animal Kingdom were analyzed. Vocalizations were recorded from microphones in collars around subject necks, and rumbles were digitized and measured using SIGNAL software. Sixteen acoustic variables were measured for each call, extracting both source and filter features. Multidimensional scaling analysis indicates that there are no acoustically distinct rumble subtypes, but that there is quantitative variation across rumbles. Discriminant function analysis showed that the acoustic characteristics of rumbles differ across females. A classification success rate of 65% was achieved when assigning unselected rumbles to one of the six females (test set =64 calls) according to the functions derived from the originally selected calls (training set =205 calls). The rumble is best viewed as a single call type with graded variation, but information regarding individual identity is encoded in female rumbles.

Data-driven automated acoustic analysis of human infant vocalizations using neural network tools.

PubMed

Warlaumont, Anne S; Oller, D Kimbrough; Buder, Eugene H; Dale, Rick; Kozma, Robert

2010-04-01

Acoustic analysis of infant vocalizations has typically employed traditional acoustic measures drawn from adult speech acoustics, such as f(0), duration, formant frequencies, amplitude, and pitch perturbation. Here an alternative and complementary method is proposed in which data-derived spectrographic features are central. 1-s-long spectrograms of vocalizations produced by six infants recorded longitudinally between ages 3 and 11 months are analyzed using a neural network consisting of a self-organizing map and a single-layer perceptron. The self-organizing map acquires a set of holistic, data-derived spectrographic receptive fields. The single-layer perceptron receives self-organizing map activations as input and is trained to classify utterances into prelinguistic phonatory categories (squeal, vocant, or growl), identify the ages at which they were produced, and identify the individuals who produced them. Classification performance was significantly better than chance for all three classification tasks. Performance is compared to another popular architecture, the fully supervised multilayer perceptron. In addition, the network's weights and patterns of activation are explored from several angles, for example, through traditional acoustic measurements of the network's receptive fields. Results support the use of this and related tools for deriving holistic acoustic features directly from infant vocalization data and for the automatic classification of infant vocalizations.

Micropower Mixed-signal VLSI Independent Component Analysis for Gradient Flow Acoustic Source Separation.

PubMed

Stanaćević, Milutin; Li, Shuo; Cauwenberghs, Gert

2016-07-01

A parallel micro-power mixed-signal VLSI implementation of independent component analysis (ICA) with reconfigurable outer-product learning rules is presented. With the gradient sensing of the acoustic field over a miniature microphone array as a pre-processing method, the proposed ICA implementation can separate and localize up to 3 sources in mild reverberant environment. The ICA processor is implemented in 0.5 µm CMOS technology and occupies 3 mm × 3 mm area. At 16 kHz sampling rate, ASIC consumes 195 µW power from a 3 V supply. The outer-product implementation of natural gradient and Herault-Jutten ICA update rules demonstrates comparable performance to benchmark FastICA algorithm in ideal conditions and more robust performance in noisy and reverberant environment. Experiments demonstrate perceptually clear separation and precise localization over wide range of separation angles of two speech sources presented through speakers positioned at 1.5 m from the array on a conference room table. The presented ASIC leads to a extreme small form factor and low power consumption microsystem for source separation and localization required in applications like intelligent hearing aids and wireless distributed acoustic sensor arrays.

Translational-circular scanning for magneto-acoustic tomography with current injection.

PubMed

Wang, Shigang; Ma, Ren; Zhang, Shunqi; Yin, Tao; Liu, Zhipeng

2016-01-27

Magneto-acoustic tomography with current injection involves using electrical impedance imaging technology. To explore the potential applications in imaging biological tissue and enhance image quality, a new scan mode for the transducer is proposed that is based on translational and circular scanning to record acoustic signals from sources. An imaging algorithm to analyze these signals is developed in respect to this alternative scanning scheme. Numerical simulations and physical experiments were conducted to evaluate the effectiveness of this scheme. An experiment using a graphite sheet as a tissue-mimicking phantom medium was conducted to verify simulation results. A pulsed voltage signal was applied across the sample, and acoustic signals were recorded as the transducer performed stepped translational or circular scans. The imaging algorithm was used to obtain an acoustic-source image based on the signals. In simulations, the acoustic-source image is correlated with the conductivity at the sample boundaries of the sample, but image results change depending on distance and angular aspect of the transducer. In general, as angle and distance decreases, the image quality improves. Moreover, experimental data confirmed the correlation. The acoustic-source images resulting from the alternative scanning mode has yielded the outline of a phantom medium. This scan mode enables improvements to be made in the sensitivity of the detecting unit and a change to a transducer array that would improve the efficiency and accuracy of acoustic-source images.

Acoustic microscope surface inspection system and method

DOEpatents

Khuri-Yakub, Butrus T.; Parent, Philippe; Reinholdtsen, Paul A.

1991-01-01

An acoustic microscope surface inspection system and method in which pulses of high frequency electrical energy are applied to a transducer which forms and focuses acoustic energy onto a selected location on the surface of an object and receives energy from the location and generates electrical pulses. The phase of the high frequency electrical signal pulses are stepped with respected to the phase of a reference signal at said location. An output signal is generated which is indicative of the surface of said selected location. The object is scanned to provide output signals representative of the surface at a plurality of surface locations.

Multichannel analysis of surface-waves and integration of downhole acoustic televiewer imaging, ultrasonic Vs and Vp, and vertical seismic profiling in an NEHRP-standard classification, South of Concordia, Kansas, USA

NASA Astrophysics Data System (ADS)

Raef, Abdelmoneam; Gad, Sabreen; Tucker-Kulesza, Stacey

2015-10-01

Seismic site characteristics, as pertaining to earthquake hazard reduction, are a function of the subsurface elastic moduli and the geologic structures. This study explores how multiscale (surface, downhole, and laboratory) datasets can be utilized to improve "constrained" average Vs30 (shear-wave velocity to a 30-meter depth). We integrate borehole, surface and laboratory measurements for a seismic site classification based on the standards of the National Earthquake Hazard Reduction Program (NEHRP). The seismic shear-wave velocity (Vs30) was derived from a geophysical inversion workflow that utilized multichannel analysis of surface-waves (MASW) and downhole acoustic televiewer imaging (DATI). P-wave and S-wave velocities, based on laboratory measurements of arrival times of ultrasonic-frequency signals, supported the workflow by enabling us to calculate Poisson's ratio, which was incorporated in building an initial model for the geophysical inversion of MASW. Extraction of core samples from two boreholes provided lithology and thickness calibration of the amplitudes of the acoustic televiewer imaging for each layer. The MASW inversion, for calculating Vs sections, was constrained with both ultrasonic laboratory measurements (from first arrivals of Vs and Vp waveforms at simulated in situ overburden stress conditions) and the downhole acoustic televiewer (DATV) amplitude logs. The Vs30 calculations enabled categorizing the studied site as NEHRP-class "C" - very dense soil and soft rock. Unlike shallow fractured carbonates in the studied area, S-wave and P-wave velocities at ultrasonic frequency for the deeper intact shale core-samples from two boreholes were in better agreement with the corresponding velocities from both a zero-offset vertical seismic profiling (VSP) and inversion of Rayleigh-wave velocity dispersion curves.

Acoustic Data Processing and Transient Signal Analysis for the Hybrid Wing Body 14- by 22-Foot Subsonic Wind Tunnel Test

NASA Technical Reports Server (NTRS)

Bahr, Christopher J.; Brooks, Thomas F.; Humphreys, William M.; Spalt, Taylor B.; Stead, Daniel J.

2014-01-01

An advanced vehicle concept, the HWB N2A-EXTE aircraft design, was tested in NASA Langley's 14- by 22-Foot Subsonic Wind Tunnel to study its acoustic characteristics for var- ious propulsion system installation and airframe con gurations. A signi cant upgrade to existing data processing systems was implemented, with a focus on portability and a re- duction in turnaround time. These requirements were met by updating codes originally written for a cluster environment and transferring them to a local workstation while en- abling GPU computing. Post-test, additional processing of the time series was required to remove transient hydrodynamic gusts from some of the microphone time series. A novel automated procedure was developed to analyze and reject contaminated blocks of data, under the assumption that the desired acoustic signal of interest was a band-limited sta- tionary random process, and of lower variance than the hydrodynamic contamination. The procedure is shown to successfully identify and remove contaminated blocks of data and retain the desired acoustic signal. Additional corrections to the data, mainly background subtraction, shear layer refraction calculations, atmospheric attenuation and microphone directivity corrections, were all necessary for initial analysis and noise assessments. These were implemented for the post-processing of spectral data, and are shown to behave as expected.

Acoustic interference and recognition space within a complex assemblage of dendrobatid frogs

PubMed Central

Amézquita, Adolfo; Flechas, Sandra Victoria; Lima, Albertina Pimentel; Gasser, Herbert; Hödl, Walter

2011-01-01

In species-rich assemblages of acoustically communicating animals, heterospecific sounds may constrain not only the evolution of signal traits but also the much less-studied signal-processing mechanisms that define the recognition space of a signal. To test the hypothesis that the recognition space is optimally designed, i.e., that it is narrower toward the species that represent the higher potential for acoustic interference, we studied an acoustic assemblage of 10 diurnally active frog species. We characterized their calls, estimated pairwise correlations in calling activity, and, to model the recognition spaces of five species, conducted playback experiments with 577 synthetic signals on 531 males. Acoustic co-occurrence was not related to multivariate distance in call parameters, suggesting a minor role for spectral or temporal segregation among species uttering similar calls. In most cases, the recognition space overlapped but was greater than the signal space, indicating that signal-processing traits do not act as strictly matched filters against sounds other than homospecific calls. Indeed, the range of the recognition space was strongly predicted by the acoustic distance to neighboring species in the signal space. Thus, our data provide compelling evidence of a role of heterospecific calls in evolutionarily shaping the frogs' recognition space within a complex acoustic assemblage without obvious concomitant effects on the signal. PMID:21969562

Deciphering acoustic emission signals in drought stressed branches: the missing link between source and sensor.

PubMed

Vergeynst, Lidewei L; Sause, Markus G R; Hamstad, Marvin A; Steppe, Kathy

2015-01-01

When drought occurs in plants, acoustic emission (AE) signals can be detected, but the actual causes of these signals are still unknown. By analyzing the waveforms of the measured signals, it should, however, be possible to trace the characteristics of the AE source and get information about the underlying physiological processes. A problem encountered during this analysis is that the waveform changes significantly from source to sensor and lack of knowledge on wave propagation impedes research progress made in this field. We used finite element modeling and the well-known pencil lead break source to investigate wave propagation in a branch. A cylindrical rod of polyvinyl chloride was first used to identify the theoretical propagation modes. Two wave propagation modes could be distinguished and we used the finite element model to interpret their behavior in terms of source position for both the PVC rod and a wooden rod. Both wave propagation modes were also identified in drying-induced signals from woody branches, and we used the obtained insights to provide recommendations for further AE research in plant science.

Acoustic emission evolution during sliding friction of Hadfield steel single crystal

NASA Astrophysics Data System (ADS)

Lychagin, D. V.; Novitskaya, O. S.; Kolubaev, A. V.; Sizova, O. V.

2017-12-01

Friction is a complex dynamic process. Direct observation of processes occurring in the friction zone is impossible due to a small size of a real contact area and, as a consequence, requires various additional methods applicable to monitor a tribological contact state. One of such methods consists in the analysis of acoustic emission data of a tribological contact. The use of acoustic emission entails the problem of interpreting physical sources of signals. In this paper, we analyze the evolution of acoustic emission signal frames in friction of Hadfield steel single crystals. The chosen crystallographic orientation of single crystals enables to identify four stages related to friction development as well as acoustic emission signals inherent in these stages. Acoustic emission signal parameters are studied in more detail by the short-time Fourier transform used to determine the time variation of the median frequency and its power spectrum. The results obtained will facilitate the development of a more precise method to monitor the tribological contact based on the acoustic emission method.

Interaction of electromagnetic and acoustic waves in a stochastic atmosphere

NASA Technical Reports Server (NTRS)

Bhatnagar, N.; Peterson, A. M.

1979-01-01

In the Stanford radio acoustic sounding system (RASS) an electromagnetic signal is made to scatter from a moving acoustic pulse train. Under a Bragg-scatter condition maximum electromagnetic scattering occurs. The scattered radio signal contains temperature and wind information as a function of the acoustic-pulse position. In this investigation RASS performance is assessed in an atmosphere characterized by the presence of turbulence and mean atmospheric parameters. The only assumption made is that the electromagnetic wave is not affected by stochastic perturbations in the atmosphere. It is concluded that the received radio signal depends strongly on the intensity of turbulence for altitudes of the acoustic pulse greater than the coherence length of propagation. The effect of mean vertical wind and mean temperature on the strength of the received signal is also demonstrated to be insignificant. Mean horizontal winds, however, shift the focus of the reflected electromagnetic energy from its origin, resulting in a decrease in received signal level when a monostatic radio-frequency (RF) system is used. For a bistatic radar configuration with space diversified receiving antennas, the shifting of the acoustic pulse makes possible the remote measurement of the horizontal wind component.

Application of multiple signal classification algorithm to frequency estimation in coherent dual-frequency lidar

NASA Astrophysics Data System (ADS)

Li, Ruixiao; Li, Kun; Zhao, Changming

2018-01-01

Coherent dual-frequency Lidar (CDFL) is a new development of Lidar which dramatically enhances the ability to decrease the influence of atmospheric interference by using dual-frequency laser to measure the range and velocity with high precision. Based on the nature of CDFL signals, we propose to apply the multiple signal classification (MUSIC) algorithm in place of the fast Fourier transform (FFT) to estimate the phase differences in dual-frequency Lidar. In the presence of Gaussian white noise, the simulation results show that the signal peaks are more evident when using MUSIC algorithm instead of FFT in condition of low signal-noise-ratio (SNR), which helps to improve the precision of detection on range and velocity, especially for the long distance measurement systems.

Acoustic data transmission through a drill string

DOEpatents

Drumheller, D.S.

1988-04-21

Acoustical signals are transmitted through a drill string by canceling upward moving acoustical noise and by preconditioning the data in recognition of the comb filter impedance characteristics of the drill string. 5 figs.

Acoustic Source Bearing Estimation (ASBE) computer program development

NASA Technical Reports Server (NTRS)

Wiese, Michael R.

1987-01-01

A new bearing estimation algorithm (Acoustic Source Analysis Technique - ASAT) and an acoustic analysis computer program (Acoustic Source Bearing Estimation - ASBE) are described, which were developed by Computer Sciences Corporation for NASA Langley Research Center. The ASBE program is used by the Acoustics Division/Applied Acoustics Branch and the Instrument Research Division/Electro-Mechanical Instrumentation Branch to analyze acoustic data and estimate the azimuths from which the source signals radiated. Included are the input and output from a benchmark test case.

Theoretical detection threshold of the proton-acoustic range verification technique.

PubMed

Ahmad, Moiz; Xiang, Liangzhong; Yousefi, Siavash; Xing, Lei

2015-10-01

Range verification in proton therapy using the proton-acoustic signal induced in the Bragg peak was investigated for typical clinical scenarios. The signal generation and detection processes were simulated in order to determine the signal-to-noise limits. An analytical model was used to calculate the dose distribution and local pressure rise (per proton) for beams of different energy (100 and 160 MeV) and spot widths (1, 5, and 10 mm) in a water phantom. In this method, the acoustic waves propagating from the Bragg peak were generated by the general 3D pressure wave equation implemented using a finite element method. Various beam pulse widths (0.1-10 μs) were simulated by convolving the acoustic waves with Gaussian kernels. A realistic PZT ultrasound transducer (5 cm diameter) was simulated with a Butterworth bandpass filter with consideration of random noise based on a model of thermal noise in the transducer. The signal-to-noise ratio on a per-proton basis was calculated, determining the minimum number of protons required to generate a detectable pulse. The maximum spatial resolution of the proton-acoustic imaging modality was also estimated from the signal spectrum. The calculated noise in the transducer was 12-28 mPa, depending on the transducer central frequency (70-380 kHz). The minimum number of protons detectable by the technique was on the order of 3-30 × 10(6) per pulse, with 30-800 mGy dose per pulse at the Bragg peak. Wider pulses produced signal with lower acoustic frequencies, with 10 μs pulses producing signals with frequency less than 100 kHz. The proton-acoustic process was simulated using a realistic model and the minimal detection limit was established for proton-acoustic range validation. These limits correspond to a best case scenario with a single large detector with no losses and detector thermal noise as the sensitivity limiting factor. Our study indicated practical proton-acoustic range verification may be feasible with approximately 5 �

Levitation of objects using acoustic energy

NASA Technical Reports Server (NTRS)

Whymark, R. R.

1975-01-01

Activated sound source establishes standing-wave pattern in gap between source and acoustic reflector. Solid or liquid material introduced in region will move to one of the low pressure areas produced at antinodes and remain suspended as long as acoustic signal is present.

Remote Acoustic Monitoring of North Atlantic Right Whales (Eubalaena glacialis) Reveals Seasonal and Diel Variations in Acoustic Behavior

PubMed Central

Matthews, Leanna P.; McCordic, Jessica A.; Parks, Susan E.

2014-01-01

Remote acoustic monitoring is a non-invasive tool that can be used to study the distribution, behavior, and habitat use of sound-producing species. The North Atlantic right whale (Eubalaena glacialis) is an endangered baleen whale species that produces a variety of stereotyped acoustic signals. One of these signals, the “gunshot” sound, has only been recorded from adult male North Atlantic right whales and is thought to function for reproduction, either as reproductive advertisement for females or as an agonistic signal toward other males. This study uses remote acoustic monitoring to analyze the presence of gunshots over a two-year period at two sites on the Scotian Shelf to determine if there is evidence that North Atlantic right whales may use these locations for breeding activities. Seasonal analyses at both locations indicate that gunshot sound production is highly seasonal, with an increase in the autumn. One site, Roseway West, had significantly more gunshot sounds overall and exhibited a clear diel trend in production of these signals at night. The other site, Emerald South, also showed a seasonal increase in gunshot production during the autumn, but did not show any significant diel trend. This difference in gunshot signal production at the two sites indicates variation either in the number or the behavior of whales at each location. The timing of the observed seasonal increase in gunshot sound production is consistent with the current understanding of the right whale breeding season, and our results demonstrate that detection of gunshots with remote acoustic monitoring can be a reliable way to track shifts in distribution and changes in acoustic behavior including possible mating activities. PMID:24646524

Remote acoustic monitoring of North Atlantic right whales (Eubalaena glacialis) reveals seasonal and diel variations in acoustic behavior.

PubMed

Matthews, Leanna P; McCordic, Jessica A; Parks, Susan E

2014-01-01

Remote acoustic monitoring is a non-invasive tool that can be used to study the distribution, behavior, and habitat use of sound-producing species. The North Atlantic right whale (Eubalaena glacialis) is an endangered baleen whale species that produces a variety of stereotyped acoustic signals. One of these signals, the "gunshot" sound, has only been recorded from adult male North Atlantic right whales and is thought to function for reproduction, either as reproductive advertisement for females or as an agonistic signal toward other males. This study uses remote acoustic monitoring to analyze the presence of gunshots over a two-year period at two sites on the Scotian Shelf to determine if there is evidence that North Atlantic right whales may use these locations for breeding activities. Seasonal analyses at both locations indicate that gunshot sound production is highly seasonal, with an increase in the autumn. One site, Roseway West, had significantly more gunshot sounds overall and exhibited a clear diel trend in production of these signals at night. The other site, Emerald South, also showed a seasonal increase in gunshot production during the autumn, but did not show any significant diel trend. This difference in gunshot signal production at the two sites indicates variation either in the number or the behavior of whales at each location. The timing of the observed seasonal increase in gunshot sound production is consistent with the current understanding of the right whale breeding season, and our results demonstrate that detection of gunshots with remote acoustic monitoring can be a reliable way to track shifts in distribution and changes in acoustic behavior including possible mating activities.

Acoustic emission intrusion detector

DOEpatents

Carver, Donald W.; Whittaker, Jerry W.

1980-01-01

An intrusion detector is provided for detecting a forcible entry into a secured structure while minimizing false alarms. The detector uses a piezoelectric crystal transducer to sense acoustic emissions. The transducer output is amplified by a selectable gain amplifier to control the sensitivity. The rectified output of the amplifier is applied to a Schmitt trigger circuit having a preselected threshold level to provide amplitude discrimination. Timing circuitry is provided which is activated by successive pulses from the Schmitt trigger which lie within a selected time frame for frequency discrimination. Detected signals having proper amplitude and frequency trigger an alarm within the first complete cycle time of a detected acoustical disturbance signal.

Generation of thermo-acoustic waves from pulsed solar/IR radiation

NASA Astrophysics Data System (ADS)

Rahman, Aowabin

Acoustic waves could potentially be used in a wide range of engineering applications; however, the high energy consumption in generating acoustic waves from electrical energy and the cost associated with the process limit the use of acoustic waves in industrial processes. Acoustic waves converted from solar radiation provide a feasible way of obtaining acoustic energy, without relying on conventional nonrenewable energy sources. One of the goals of this thesis project was to experimentally study the conversion of thermal to acoustic energy using pulsed radiation. The experiments were categorized into "indoor" and "outdoor" experiments, each with a separate experimental setup. The indoor experiments used an IR heater to power the thermo-acoustic lasers and were primarily aimed at studying the effect of various experimental parameters on the amplitude of sound waves in the low frequency range (below 130 Hz). The IR radiation was modulated externally using a chopper wheel and then impinged on a porous solid, which was housed inside a thermo-acoustic (TA) converter. A microphone located at a certain distance from the porous solid inside the TA converter detected the acoustic signals. The "outdoor" experiments, which were targeted at TA conversion at comparatively higher frequencies (in 200 Hz-3 kHz range) used solar energy to power the thermo-acoustic laser. The amplitudes (in RMS) of thermo-acoustic signals obtained in experiments using IR heater as radiation source were in the 80-100 dB range. The frequency of acoustic waves corresponded to the frequency of interceptions of the radiation beam by the chopper. The amplitudes of acoustic waves were influenced by several factors, including the chopping frequency, magnitude of radiation flux, type of porous material, length of porous material, external heating of the TA converter housing, location of microphone within the air column, and design of the TA converter. The time-dependent profile of the thermo-acoustic signals

Method and apparatus for using magneto-acoustic remanence to determine embrittlement

NASA Technical Reports Server (NTRS)

Allison, Sidney G. (Inventor); Namkung, Min (Inventor); Yost, William T. (Inventor); Cantrell, John H. (Inventor)

1992-01-01

A method and apparatus for testing steel components for temperature embrittlement uses magneto-acoustic emission to nondestructively evaluate the component are presented. Acoustic emission signals occur more frequently at higher levels in embrittled components. A pair of electromagnets are used to create magnetic induction in the test component. Magneto-acoustic emission signals may be generated by applying an AC current to the electromagnets. The acoustic emission signals are analyzed to provide a comparison between a component known to be unembrittled and a test component. Magnetic remanence is determined by applying a DC current to the electromagnets and then by turning the magnets off and observing the residual magnetic induction.

Acoustic emission-based sensor analysis and damage classification for structural health monitoring of composite structures

NASA Astrophysics Data System (ADS)

Uprety, Bibhisha

Within the aerospace industry the need to detect and locate impact events, even when no visible damage is present, is important both from the maintenance and design perspectives. This research focused on the use of Acoustic Emission (AE) based sensing technologies to identify impact events and characterize damage modes in composite structures for structural health monitoring. Six commercially available piezoelectric AE sensors were evaluated for use with impact location estimation algorithms under development at the University of Utah. Both active and passive testing were performed to estimate the time of arrival and plate wave mode velocities for impact location estimation. Four sensors were recommended for further comparative investigations. Furthermore, instrumented low-velocity impact experiments were conducted on quasi-isotropic carbon/epoxy composite laminates to initiate specific types of damage: matrix cracking, delamination and fiber breakage. AE signal responses were collected during impacting and the test panels were ultrasonically C-scanned after impact to identify the internal damage corresponding to the AE signals. Matrix cracking and delamination damage produced using more compliant test panels and larger diameter impactor were characterized by lower frequency signals while fiber breakage produced higher frequency responses. The results obtained suggest that selected characteristics of sensor response signals can be used both to determine whether damage is produced during impacting and to characterize the types of damage produced in an impacted composite structure.

NEMO-SMO acoustic array: A deep-sea test of a novel acoustic positioning system for a km3-scale underwater neutrino telescope

NASA Astrophysics Data System (ADS)

Viola, S.; Ardid, M.; Bertin, V.; Enzenhöfer, A.; Keller, P.; Lahmann, R.; Larosa, G.; Llorens, C. D.; NEMO Collaboration; SMO Collaboration

2013-10-01

Within the activities of the NEMO project, the installation of a 8-floors tower (NEMO-Phase II) at a depth of 3500 m is foreseen in 2012. The tower will be installed about 80 km off-shore Capo Passero, in Sicily. On board the NEMO tower, an array of 18 acoustic sensors will be installed, permitting acoustic detection of biological sources, studies for acoustic neutrino detection and primarily acoustic positioning of the underwater structures. For the latter purpose, the sensors register acoustic signals emitted by five acoustic beacons anchored on the sea-floor. The data acquisition system of the acoustic sensors is fully integrated with the detector data transport system and is based on an “all data to shore” philosophy. Signals coming from hydrophones are continuously sampled underwater at 192 kHz/24 bit and transmitted to shore through an electro-optical cable for real-time analysis. A novel technology for underwater GPS time-stamping of data has been implemented and tested. The operation of the acoustic array will permit long-term test of sensors and electronics technologies that are proposed for the acoustic positioning system of KM3NeT.

Acoustic microscope surface inspection system and method

DOEpatents

Khuri-Yakub, B.T.; Parent, P.; Reinholdtsen, P.A.

1991-02-26

An acoustic microscope surface inspection system and method are described in which pulses of high frequency electrical energy are applied to a transducer which forms and focuses acoustic energy onto a selected location on the surface of an object and receives energy from the location and generates electrical pulses. The phase of the high frequency electrical signal pulses are stepped with respect to the phase of a reference signal at said location. An output signal is generated which is indicative of the surface of said selected location. The object is scanned to provide output signals representative of the surface at a plurality of surface locations. 7 figures.

Analyzing the acoustic beat with mobile devices

NASA Astrophysics Data System (ADS)

Kuhn, Jochen; Vogt, Patrik; Hirth, Michael

2014-04-01

In this column, we have previously presented various examples of how physical relationships can be examined by analyzing acoustic signals using smartphones or tablet PCs. In this example, we will be exploring the acoustic phenomenon of small beats, which is produced by the overlapping of two tones with a low difference in frequency Δf. The resulting auditory sensation is a tone with a volume that varies periodically. Acoustic beats can be perceived repeatedly in day-to-day life and have some interesting applications. For example, string instruments are still tuned with the help of an acoustic beat, even with modern technology. If a reference tone (e.g., 440 Hz) and, for example, a slightly out-of-tune violin string produce a tone simultaneously, a beat can be perceived. The more similar the frequencies, the longer the duration of the beat. In the extreme case, when the frequencies are identical, a beat no longer arises. The string is therefore correctly tuned. Using the Oscilloscope app,4 it is possible to capture and save acoustic signals of this kind and determine the beat frequency fS of the signal, which represents the difference in frequency Δf of the two overlapping tones (for Android smartphones, the app OsciPrime Oscilloscope can be used).

Iso-acoustic focusing of cells for size-insensitive acousto-mechanical phenotyping

PubMed Central

Augustsson, Per; Karlsen, Jonas T.; Su, Hao-Wei; Bruus, Henrik; Voldman, Joel

2016-01-01

Mechanical phenotyping of single cells is an emerging tool for cell classification, enabling assessment of effective parameters relating to cells' interior molecular content and structure. Here, we present iso-acoustic focusing, an equilibrium method to analyze the effective acoustic impedance of single cells in continuous flow. While flowing through a microchannel, cells migrate sideways, influenced by an acoustic field, into streams of increasing acoustic impedance, until reaching their cell-type specific point of zero acoustic contrast. We establish an experimental procedure and provide theoretical justifications and models for iso-acoustic focusing. We describe a method for providing a suitable acoustic contrast gradient in a cell-friendly medium, and use acoustic forces to maintain that gradient in the presence of destabilizing forces. Applying this method we demonstrate iso-acoustic focusing of cell lines and leukocytes, showing that acoustic properties provide phenotypic information independent of size. PMID:27180912

Iso-acoustic focusing of cells for size-insensitive acousto-mechanical phenotyping.

PubMed

Augustsson, Per; Karlsen, Jonas T; Su, Hao-Wei; Bruus, Henrik; Voldman, Joel

2016-05-16

Mechanical phenotyping of single cells is an emerging tool for cell classification, enabling assessment of effective parameters relating to cells' interior molecular content and structure. Here, we present iso-acoustic focusing, an equilibrium method to analyze the effective acoustic impedance of single cells in continuous flow. While flowing through a microchannel, cells migrate sideways, influenced by an acoustic field, into streams of increasing acoustic impedance, until reaching their cell-type specific point of zero acoustic contrast. We establish an experimental procedure and provide theoretical justifications and models for iso-acoustic focusing. We describe a method for providing a suitable acoustic contrast gradient in a cell-friendly medium, and use acoustic forces to maintain that gradient in the presence of destabilizing forces. Applying this method we demonstrate iso-acoustic focusing of cell lines and leukocytes, showing that acoustic properties provide phenotypic information independent of size.

Refinement and application of acoustic impulse technique to study nozzle transmission characteristics

NASA Technical Reports Server (NTRS)

Salikuddin, M.; Brown, W. H.; Ramakrishnan, R.; Tanna, H. K.

1983-01-01

An improved acoustic impulse technique was developed and was used to study the transmission characteristics of duct/nozzle systems. To accomplish the above objective, various problems associated with the existing spark-discharge impulse technique were first studied. These included (1) the nonlinear behavior of high intensity pulses, (2) the contamination of the signal with flow noise, (3) low signal-to-noise ratio at high exhaust velocities, and (4) the inability to control or shape the signal generated by the source, specially when multiple spark points were used as the source. The first step to resolve these problems was the replacement of the spark-discharge source with electroacoustic driver(s). These included (1) synthesizing on acoustic impulse with acoustic driver(s) to control and shape the output signal, (2) time domain signal averaging to remove flow noise from the contaminated signal, (3) signal editing to remove unwanted portions of the time history, (4) spectral averaging, and (5) numerical smoothing. The acoustic power measurement technique was improved by taking multiple induct measurements and by a modal decomposition process to account for the contribution of higher order modes in the power computation. The improved acoustic impulse technique was then validated by comparing the results derived by an impedance tube method. The mechanism of acoustic power loss, that occurs when sound is transmitted through nozzle terminations, was investigated. Finally, the refined impulse technique was applied to obtain more accurate results for the acoustic transmission characteristics of a conical nozzle and a multi-lobe multi-tube supressor nozzle.

Information Theory Filters for Wavelet Packet Coefficient Selection with Application to Corrosion Type Identification from Acoustic Emission Signals

PubMed Central

Van Dijck, Gert; Van Hulle, Marc M.

2011-01-01

The damage caused by corrosion in chemical process installations can lead to unexpected plant shutdowns and the leakage of potentially toxic chemicals into the environment. When subjected to corrosion, structural changes in the material occur, leading to energy releases as acoustic waves. This acoustic activity can in turn be used for corrosion monitoring, and even for predicting the type of corrosion. Here we apply wavelet packet decomposition to extract features from acoustic emission signals. We then use the extracted wavelet packet coefficients for distinguishing between the most important types of corrosion processes in the chemical process industry: uniform corrosion, pitting and stress corrosion cracking. The local discriminant basis selection algorithm can be considered as a standard for the selection of the most discriminative wavelet coefficients. However, it does not take the statistical dependencies between wavelet coefficients into account. We show that, when these dependencies are ignored, a lower accuracy is obtained in predicting the corrosion type. We compare several mutual information filters to take these dependencies into account in order to arrive at a more accurate prediction. PMID:22163921

Pipe wall damage detection by electromagnetic acoustic transducer generated guided waves in absence of defect signals.

PubMed

Vasiljevic, Milos; Kundu, Tribikram; Grill, Wolfgang; Twerdowski, Evgeny

2008-05-01

Most investigators emphasize the importance of detecting the reflected signal from the defect to determine if the pipe wall has any damage and to predict the damage location. However, often the small signal from the defect is hidden behind the other arriving wave modes and signal noise. To overcome the difficulties associated with the identification of the small defect signal in the time history plots, in this paper the time history is analyzed well after the arrival of the first defect signal, and after different wave modes have propagated multiple times through the pipe. It is shown that the defective pipe can be clearly identified by analyzing these late arriving diffuse ultrasonic signals. Multiple reflections and scattering of the propagating wave modes by the defect and pipe ends do not hamper the defect detection capability; on the contrary, it apparently stabilizes the signal and makes it easier to distinguish the defective pipe from the defect-free pipe. This paper also highlights difficulties associated with the interpretation of the recorded time histories due to mode conversion by the defect. The design of electro-magnetic acoustic transducers used to generate and receive the guided waves in the pipe is briefly described in the paper.

Digital seismo-acoustic signal processing aboard a wireless sensor platform

NASA Astrophysics Data System (ADS)

Marcillo, O.; Johnson, J. B.; Lorincz, K.; Werner-Allen, G.; Welsh, M.

2006-12-01

We are developing a low power, low-cost wireless sensor array to conduct real-time signal processing of earthquakes at active volcanoes. The sensor array, which integrates data from both seismic and acoustic sensors, is based on Moteiv TMote Sky wireless sensor nodes (www.moteiv.com). The nodes feature a Texas Instruments MSP430 microcontroller, 48 Kbytes of program memory, 10 Kbytes of static RAM, 1 Mbyte of external flash memory, and a 2.4-GHz Chipcon CC2420 IEEE 802.15.4 radio. The TMote Sky is programmed in TinyOS. Basic signal processing occurs on an array of three peripheral sensor nodes. These nodes are tied into a dedicated GPS receiver node, which is focused on time synchronization, and a central communications node, which handles data integration and additional processing. The sensor nodes incorporate dual 12-bit digitizers sampling a seismic sensor and a pressure transducer at 100 samples per second. The wireless capabilities of the system allow flexible array geometry, with a maximum aperture of 200m. We have already developed the digital signal processing routines on board the Moteiv Tmote sensor nodes. The developed routines accomplish Real-time Seismic-Amplitude Measurement (RSAM), Seismic Spectral- Amplitude Measurement (SSAM), and a user-configured Short Term Averaging / Long Term Averaging (STA LTA ratio), which is used to calculate first arrivals. The processed data from individual nodes are transmitted back to a central node, where additional processing may be performed. Such processing will include back azimuth determination and other wave field analyses. Future on-board signal processing will focus on event characterization utilizing pattern recognition and spectral characterization. The processed data is intended as low bandwidth information which can be transmitted periodically and at low cost through satellite telemetry to a web server. The processing is limited by the computational capabilities (RAM, ROM) of the nodes. Nevertheless, we

High-sensitivity fiber optic acoustic sensors

NASA Astrophysics Data System (ADS)

Lu, Ping; Liu, Deming; Liao, Hao

2016-11-01

Due to the overwhelming advantages compared with traditional electronicsensors, fiber-optic acoustic sensors have arisen enormous interest in multiple disciplines. In this paper we present the recent research achievements of our group on fiber-optic acoustic sensors. The main point of our research is high sensitivity interferometric acoustic sensors, including Michelson, Sagnac, and Fabry-Pérot interferometers. In addition, some advanced technologies have been proposed for acoustic or acoustic pressure sensing such as single-mode/multimode fiber coupler, dual FBGs and multi-longitudinal mode fiber laser based acoustic sensors. Moreover, our attention we have also been paid on signal demodulation schemes. The intensity-based quadrature point (Q-point) demodulation, two-wavelength quadrature demodulation and symmetric 3×3 coupler methodare discussed and compared in this paper.