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Motivated by the quasi-categorical reduced forms of disyllabic words produced in Chinese conversational speech, a frequency-based selection procedure of typical pronunciation by disyllabic word type and reduction degree is proposed in this paper. This variant-selection algorithm utilizes techniques of free phone recognition and phonetic similarity score calculation to account for Chinese syllable structure. Four reduction types are suggested by considering the presence of a within-word syllable boundary: Citation form-like reduction, marginal segment deletion, nuclei merger, and syllable merger. The results show that the most frequent reduction types for disyllabic words in Chinese conversation are citation form-like reduction and syllable merger. In particular, high-frequency disyllabic words preferentially take the extreme syllable-merger form. As shown in the analysis, segmental reduction in Chinese disyllabic words is morphology-dependent. It is also related to the prosodic position at which a disyllabic word is produced as well as the temporal quality of the word. Finally, in the automatic speech recognition experiments, the performance was improved by adding a small number of variants selected by the algorithm to the pronunciation dictionary of the system. [ABSTRACT FROM AUTHOR]

As an efficient technique, microwave heating is applied for various thermal processes in recent years. Information of temperature field distribution is important to security and stability in the process of microwave heating. In this paper, we investigate a three-dimensional temperature field distribution reconstruction algorithm using ultrasonic thermometry. Four three-dimensional temperature field distribution models with different complex levels are used in the simulation experiments. The error analysis between the temperature field distribution models and the reconstructed ones indicates that the proposed reconstruction algorithm possesses high reconstruction precision. [ABSTRACT FROM AUTHOR]

In order to improve the Heart Sound recognition rate and reduce the recognition time, in this paper, we introduces a new method for Heart Sound pattern recognition by using Heart Sound Texture Map. Based on the Heart Sound model, we give the Heart Sound time-frequency diagram and the Heart Sound Texture Map definition, we study the structure of the Heart Sound Window Function principle and realization method, and then discusses how to use the Heart Sound Window Function and the Short-time Fourier Transform to obtain two-dimensional Heart Sound time-frequency diagram, propose corner correlation recognition algorithm based on the Heart Sound Texture Map according to the characteristics of Heart Sound. The simulation results show that the Heart Sound Window Function compared with the traditional window function makes the first (S1) and the second (S2) Heart Sound texture clearer. And the corner correlation recognition algorithm based on the Heart Sound Texture Map can significantly improve the recognition rate and reduce the expense, which is an effective Heart Sound recognition method. [ABSTRACT FROM AUTHOR]

The acoustic signal emitted by a turbo-prop aircraft consists of a strong narrowband tone superimposed on a broadband random component. A ground-based three-element planar acoustic array can be used to estimate the full set of flight parameters of a turbo-prop aircraft in transit by measuring the time delay (TD) between the signal received at the reference sensor and the signal received at each of the other two sensors of the array over a sufficiently long period of time. This paper studies the possibility of using instantaneous frequency (IF) measurements from the reference sensor to improve the precision of the flight parameter estimates. A simplified Cramer-Rao lower bound analysis shows that the standard deviations in the estimates of the aircraft velocity and altitude can be greatly reduced when IF measurements are used together with TD measurements. Two flight parameter estimation algorithms that utilize both IF and TD measurements are formulated and their performances are evaluated using both simulated and real data. [ABSTRACT FROM AUTHOR]

The issue of handling a leading-order cross-multiplied term in three-dimensional (3D) parabolic equation (PE) based models is addressed. In particular, numerical results obtained incorporating a leading-order cross-term correction in an existing 3D PE model, written in cylindrical coordinates, based on higher-order Pade approximations in both depth and azimuth, and a splitting operator technique are reported. Note that the numerical algorithm proposed in this paper could be used in the future to update any 3D PE codes that neglect cross terms and use a splitting numerical technique. The 3D penetrable wedge benchmark problem is chosen to illustrate the accuracy of the now-fully wide-angle enhanced 3D PE model. The comparisons with a 3D reference solution based on the image source clearly show that handling the leading-order cross term in the 3D PE computation is sufficient to remove the phase errors inherent to any 3D PE models that neglect cross terms in their formulations. [ABSTRACT FROM AUTHOR]

In this paper, a fusion of K models of full-rank weighted nonnegative tensor factor twodimensional deconvolution (K-wNTF2D) is proposed to separate the acoustic sources that have been mixed in an underdetermined reverberant environment. The model is adapted in an unsupervised manner under the hybrid framework of the generalized expectation maximization and multiplicative update algorithms. The derivation of the algorithm and the development of proposed full-rank K-wNTF2D will be shown. The algorithm also encodes a set of variable sparsity parameters derived from Gibbs distribution into the K-wNTF2D model. This optimizes each sub-model in K-wNTF2D with the required sparsity to model the time-varying variances of the sources in the spectrogram. In addition, an initialization method is proposed to initialize the parameters in the K-wNTF2D. Experimental results on the underdetermined reverberant mixing environment have shown that the proposed algorithm is effective at separating the mixture with an average signal-to-distortion ratio of 3 dB. [ABSTRACT FROM AUTHOR]

This paper introduces an eigenvector pruning algorithm for the estimation of the signal-plus-interference eigenspace, required as a preliminary step to subspace beamforming. The proposed method considers large-aperture passive array configurations operating in environments with multiple maneuvering targets in background noise, in which the available data for estimation of sample covariances and eigenvectors are limited. Based on statistical properties of scalar products between deterministic and complex random vectors, this work defines a statistically justified threshold to identify target-related features embedded in the sample eigenvectors, leading to an estimator for the signal-bearing eigenspace. It is shown that data projection into this signal subspace results in sharpening of beamforming outputs corresponding to closely spaced targets and provides better target separation compared to current subspace beamformers. In addition, the proposed threshold gives the user control over the worst-case scenario for the number of false detections by the beamformer. Simulated data are used to quantify the performance of the subspace estimator according to the distance between estimated and true signal subspaces. Beamforming resolution using the proposed method is analyzed with simulated data corresponding to a horizontal line array, as well as experimental data from the Shallow Water Array Performance experiment. [ABSTRACT FROM AUTHOR]

This paper develops and examines a modified technique for identifying damage in slender structures. An algorithm, called modified sequential algebraic algorithm, is presented to solve the longitudinal acoustic wave propagation problem in inhomogeneous media. This method accurately predicts the echo generated by the inhomogeneities when the acoustic impedance variation profile in the damaged part of the structure is non-smooth. For these cases, the available algorithms fail to provide the actual regressive wave component. In the following, a stochastic optimization scheme, the differential evolution method, is applied to solve the inverse problem. Numerical examples with smooth and non-smooth impedance profiles are considered. It is shown that, for smooth profiles, both the available techniques and the modified algorithm succeed in identifying the actual damage. Nevertheless, in situations where a rough profile is to be reconstructed, only the modified algorithm succeeds. The technique is found to be robust with respect to additive noise in the signals. [ABSTRACT FROM AUTHOR]

This paper introduces a time-variant reverberation algorithm as an extension of the feedback delay network (FDN). By modulating the feedback matrix nearly continuously over time, a complex pattern of concurrent amplitude modulations of the feedback paths evolves. Due to its complexity, the modulation produces less likely perceivable artifacts and the time-variation helps to increase the liveliness of the reverberation tail. A listening test, which has been conducted, confirms that the perceived quality of the reverberation tail can be enhanced by the feedback matrix modulation. In contrast to the prior art time-varying allpass FDNs, it is shown that unitary feedback matrix modulation is guaranteed to be stable. Analytical constraints on the pole locations of the FDN help to describe the modulation effect in depth. Further, techniques and conditions for continuous feedback matrix modulation are presented. [ABSTRACT FROM AUTHOR]

Various algorithms for determining relaxation time spectrum have been to the fore, because relaxation spectrum plays an important role in interconversion between various viscoelastic functions. In this study, a new algorithm is developed by means of double-logarithmic power series approximation and the Levenberg-Marquardt method. Compared with most previous algorithms, new algorithm is applicable not only to dynamic data but also to the Laplace transform of relaxation modulus, which is available for optical data from the generalized Stokes-Einstein equation or creep data with ringing. It is worth noticing that the new algorithm determines continuous relaxation spectrum without any additional parameters and precedent procedures. This paper suggests such framework of newly developed algorithm and its applications to various kinds of viscoelastic data. [ABSTRACT FROM AUTHOR]

Although requiring prior knowledge makes the ideal binary mask an impractical algorithm, substantial increases in measured intelligibility make it a desirable benchmark. While this benchmark has been studied extensively, many questions remain about the factors that influence the intelligibility of binary-masked speech with non-ideal masks. To date, researchers have used primarily uniformly random, uncorrelated mask errors and independently presented error types (i.e., false positives and negatives) to characterize the influence of estimation errors on intelligibility. However, practical estimation algorithms produce masks that contain errors of both types and with non-trivial amounts of structure. This paper introduces an investigation framework for binary masks and presents listener studies that use this framework to illustrate how interactions between error types and structure affect intelligibility. First, this study demonstrates that clustering (i.e., a form of structure) of mask errors reduces intelligibility. Furthermore, while previous research has suggested that false positives are more detrimental to intelligibility than false negatives, this study indicates that false negatives can be equally detrimental to intelligibility when they contain structure or when both error types are present. Finally, this study shows that listeners tolerate fewer mask errors when both types of errors are present, especially when the errors contain structure. [ABSTRACT FROM AUTHOR]

Annotation of phrases in birdsongs can be helpful to behavioral and population studies. To reduce the need for manual annotation, an automated birdsong phrase classification algorithm for limited data is developed. Limited data occur because of limited recordings or the existence of rare phrases. In this paper, classification of up to 81 phrase classes of Cassin's Vireo is performed using one to five training samples per class. The algorithm involves dynamic time warping (DTW) and two passes of sparse representation (SR) classification. DTW improves the similarity between training and test phrases from the same class in the presence of individual bird differences and phrase segmentation inconsistencies. The SR classifier works by finding a sparse linear combination of training feature vectors from all classes that best approximates the test feature vector. When the class decisions from DTW and the first pass SR classification are different, SR classification is repeated using training samples from these two conflicting classes. Compared to DTW, support vector machines, and an SR classifier without DTW, the proposed classifier achieves the highest classification accuracies of 94% and 89% on manually segmented and automatically segmented phrases, respectively, from unseen Cassin's Vireo individuals, using five training samples per class. [ABSTRACT FROM AUTHOR]

This paper proposes a sparse linear prediction based algorithm to estimate time difference of arrival. This algorithm unifies the cross correlation method without prewhitening and that with prewhitening via an ℓ2/ℓ1 optimization process, which is solved by an augmented Lagrangian alternating direction method. It also forms a set of time delay estimators that make a tradeoff between prewhitening and non-prewhitening through adjusting a regularization parameter. The effectiveness of the proposed algorithm is demonstrated in noisy and reverberant environments. [ABSTRACT FROM AUTHOR]

To retrieve complex-valued effective permittivity and permeability of electromagnetic metamaterials (EMMs) based on resonant effect from scattering parameters using a complex logarithmic function is not inevitable. When complex values are expressed in terms of magnitude and phase, an infinite number of permissible phase angles is permissible due to the multi-valued property of complex logarithmic functions. Special attention needs to be paid to ensure continuity of the effective permittivity and permeability of lossy metamaterials as frequency sweeps. In this paper, an automated phase correction (APC) algorithm is proposed to properly trace and compensate phase angles of the complex logarithmic function which may experience abrupt phase jumps near the resonant frequency region of the concerned EMMs, and hence the continuity of the effective optical properties of lossy metamaterials is ensured. The algorithm is then verified to extract effective optical properties from the simulated scattering parameters of the four different types of metamaterial media: a cut-wire cell array, a split ring resonator (SRR) cell array, an electric-LC (E-LC) resonator cell array, and a combined SRR and wire cell array respectively. The results demonstrate that the proposed algorithm is highly accurate and effective. [ABSTRACT FROM AUTHOR]

Quantitative measures of acoustic similarity can reveal patterns of shared vocal behavior in social species. Many methods for computing similarity have been developed, but their performance has not been extensively characterized in noisy environments and with vocalizations characterized by complex frequency modulations. This paper describes methods of bioacoustic comparison based on dynamic time warping (DTW) of the fundamental frequency or spectrogram. Fundamental frequency is estimated using a Bayesian particle filter adaptation of harmonic template matching. The methods were tested on field recordings of flight calls from superb starlings, Lamprotornis superbus, for how well they could separate distinct categories of call elements (motifs). The fundamental-frequency-based method performed best, but the spectrogram-based method was less sensitive to noise. Both DTW methods provided better separation of categories than spectrographic cross correlation, likely due to substantial variability in the duration of superb starling flight call motifs. [ABSTRACT FROM AUTHOR]

This paper estimates seabed shear-wave velocity profiles and their uncertainties using interface-wave dispersion curves extracted from data generated by a shear-wave source. The shear-wave source generated a seismic signature over a frequency range between 2 and 60 Hz and was polarized in both in-line and cross-line orientations. Low-frequency Scholte- and Love-waves were recorded. Dispersion curves of the Scholte- and Love-waves for the fundamental mode and higher-order modes are extracted by three time-frequency analysis methods. Both the vertically and horizontally polarized shear-wave velocity profiles in the sediment are estimated by the Scholte- and Love-wave dispersion curves, respectively. A Bayesian approach is utilized for the inversion. Differential evolution, a global search algorithm is applied to estimate the most-probable shear-velocity models. Marginal posterior probability profiles are computed by Metropolis-Hastings sampling. The estimated vertically and horizontally polarized shear-wave velocity profiles fit well with the core and in situ measurements. [ABSTRACT FROM AUTHOR]

This paper proposes a voice activity detection (VAD) method in a kernel subspace domain to improve the performance of the kernel-based VAD. A linear transform matrix that can simultaneously diagonalize the two covariance matrices using kernel principal component analysis is presented to generate the kernel subspace. The likelihood ratio test based on Gaussian distributions is applied for the VAD in the kernel subspace. Experimental results show that the proposed VAD algorithm outperforms the conventional approaches under various noise conditions. [ABSTRACT FROM AUTHOR]

A ground-based acoustic sensor array can be used to estimate the complete set of flight parameters of a jet aircraft or other airborne source of broadband sound in transit by measuring the differential time of arrival (DTOA), or the time delay, of the direct path signal at each sensor pair of the array over a sufficiently long period of time. This paper studies the possibility of using intersensor multipath delay measurements to improve the precision of the flight parameter estimates for a small aperture array, without increasing the array's intersensor spacing or the observation time for more time delay measurements. Intersensor multipath delay is defined as the DTOA of the direct path signal at one sensor and the ground-reflected path signal at another sensor. The flight parameter estimation algorithm is formulated and a simplified Cramer-Rao lower bound error analysis is presented, which shows that the standard deviations in the flight parameter estimates are greatly reduced when intersensor multipath delay measurements are used together with time delay measurements. The effectiveness of the proposed flight parameter estimation method for small aperture arrays is verified using both simulated and real data. [ABSTRACT FROM AUTHOR]

This paper introduces an approach for online speech source clustering and separation, which is based on the utilization of the multichannel location information in a recursive expectation maximization (EM) algorithm. Specifically, the normalized multichannel speech-recording vector is employed as a feature vector and is modeled using Watson mixture model. The model parameters are determined by maximizing the data likelihood at every time-frequency slot in an online processing manner. Consequently, the proposed approach can continuously adjust the speech clusters. Promising results showing the advantage of the proposed approach over the batch EM algorithm in the case of two speakers with speaker movement are obtained. [ABSTRACT FROM AUTHOR]

Acoustic channel estimation is an important problem in various applications. Unlike many existing channel estimation techniques that need known probe or training signals, this paper develops a blind multipath channel identification algorithm. The proposed approach is based on the single-input multiple-output model and exploits the sparse multichannel structure. Three sparse representation algorithms, namely, matching pursuit, orthogonal matching pursuit, and basis pursuit, are applied to the blind sparse identification problem. Compared with the classical least squares approach to blind multichannel estimation, the proposed scheme does not require that the channel order be exactly determined and it is robust to channel order selection. Moreover, the ill-conditioning induced by the large delay spread is overcome by the sparse constraint. Simulation results for deconvolution of both underwater and room acoustic channels confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This work concerns the development of field directionality mapping algorithms for short acoustic arrays on mobile maneuverable platforms that avoid the left/right ambiguities and endfire resolution degradation common to longer non-maneuverable line arrays. In this paper, it is shown that short maneuverable arrays can achieve a high fraction of usable bearing space for target detection in interference-dominated scenarios, despite their lower array gain against diffuse background noise. Two narrowband techniques are presented which use the expectation-maximization maximum likelihood algorithm under different models of the time-varying field directionality. The first, derivative based maximum likelihood, uses a deterministic model while the second, recursive Bayes maximum likelihood, uses a stochastic model for the time-varying spatial spectrum. In addition, a broadband extension is introduced that incorporates temporal spectral knowledge to suppress ambiguities when the average sensor array spacing is greater than a half-wavelength. Dynamic multi-source simulations demonstrate the ability of a short, maneuvering array to reduce array ambiguities and spatial grating lobes in an interference dominated environment. Monte Carlo evaluation of receiver operating characteristics is used to evaluate the improvement in source detection achieved by the proposed methods versus conventional broadband beamforming. [ABSTRACT FROM AUTHOR]

A time-frequency contour extraction and classification algorithm was created to analyze humpback whale vocalizations. The algorithm automatically extracted contours of whale vocalization units by searching for gray-level discontinuities in the spectrogram images. The unit-to-unit similarity was quantified by cross-correlating the contour lines. A library of distinctive humpback units was then generated by applying an unsupervised, cluster-based learning algorithm. The purpose of this study was to provide a fast and automated feature selection tool to describe the vocal signatures of animal groups. This approach could benefit a variety of applications such as species description, identification, and evolution of song structures. The algorithm was tested on humpback whale song data recorded at various locations in Hawaii from 2002 to 2003. Results presented in this paper showed low probability of false alarm (0%-4%) under noisy environments with small boat vessels and snapping shrimp. The classification algorithm was tested on a controlled set of 30 units forming six unit types, and all the units were correctly classified. In a case study on humpback data collected in the Auau Chanel, Hawaii, in 2002, the algorithm extracted 951 units, which were classified into 12 distinctive types. [ABSTRACT FROM AUTHOR]

Since the 1960s and 1970s, researchers have proposed simplified models using only a few parameters to describe the vibro-acoustical behavior of string instruments in the low-frequency range. This paper presents a method for deriving and estimating a few important parameters or features describing the mechanical behavior of classical guitars over a broader frequency range. These features are selected under the constraint that the measurements may readily be made in the workshop of an instrument maker. The computations of these features use estimates of the modal parameters over a large frequency range, made with the high-resolution subspace ESPRIT algorithm (Estimation of Signal Parameters via Rotational Invariant Techniques) and the signal enumeration technique ESTER (ESTimation of ERror). The methods are applied to experiments on real metal and wood plates and numerical simulations of them. The results on guitars show a nearly constant mode density in the mid- and high-frequency ranges, as it is found for a flat panel. Four features are chosen as characteristic parameters of this equivalent plate: Mass, rigidity, characteristic admittance, and the mobility deviation. Application to a set of 12 guitars indicates that these features are good candidates to discriminate different classes of classical guitars. [ABSTRACT FROM AUTHOR]

Noise levels in hospitals, especially neonatal intensive care units (NICUs), have become of great concern for hospital designers. This paper details an artificial neural network (ANN) approach to forecasting the sound loads in NICUs. The ANN is used to learn the relationship between past, present, and future noise levels. By training the ANN with data specific to the location and device used to measure the sound, the ANN is able to produce reasonable predictions of noise levels in the NICU. Best case results show average absolute errors of 5.06 ± 4.04 % when used to predict the noise levels one hour ahead, which correspond to 2.53 dBA ± 2.02 dBA. The ANN has the tendency to overpredict during periods of stability and underpredict during large transients. This forecasting algorithm could be of use in any application where prediction and prevention of harmful noise levels are of the utmost concern. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced multiple signal classification (MUSIC) algorithm is introduced to retrieve small three-dimensional elastic inclusions. First, the multistatic response (MSR) matrix is built by two different ways depending on considering the multiple scattering effect or not. The eigenvalue structure of the MSR matrix is analyzed to identify the degenerate case of inclusions. Second, the pseudo-spectrum function is built with an optimal test direction on each node, where a balancing technique is employed to ensure the numerical stability. Benefitting from this MUSIC indicator, the degenerate inclusions can be located with a good performance against noise. Numerical simulations show the proposed method has wider applicability, better resolution, and more robust in the presence of noise than the standard MUSIC methods. [ABSTRACT FROM AUTHOR]

This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea. [ABSTRACT FROM AUTHOR]

The distributed point source method is commonly used to predict the complex acoustic field emitted by ultrasonic transducers. In this paper, it is presented as an alternative to conventional approaches often used when solving rough surface scattering problems. Surface shadowing and multiple scattering effects are inherently included in the mesh-free semi-analytical simulation method through matrix manipulation making it very efficient and simple to implement. Results are presented which illustrate the improvement in accuracy gained over the Kirchhoff approximation and the decrease in computational load over the finite element method, culminating in greater than an order of magnitude decrease in required simulation time. The method is applied to the practical problem of online wall thickness monitoring within corrosive environments, illustrating the variability in reflected pulse shape that could be expected from rough surfaces with similar statistics. Three commonly implemented time-of-flight algorithms are used to analyze a large number of simulated signals from which it is concluded that those based on first arrival time are more stable under increasing roughness conditions than those which are based on reflected pulse shape. [ABSTRACT FROM AUTHOR]

The near field signal redundancy algorithm for phase-aberration correction is sensitive to tissue motion because several separated transmissions are usually needed to acquire a set of common-midpoint signals. If tissues are moving significantly due to, for example, heart beats, the effects of tissue motion on common-midpoint signals need to be corrected before the phase-aberration profile can be successfully measured. Theoretical analyses in this paper show that the arrival-time difference between a pair of common-midpoint signals due to tissue motion is usually very similar to that between the pair of reciprocal signals acquired using the same two transmissions. Based on this conclusion, an algorithm for correcting tissue-motion effects on the peak position of cross-correlation functions between common-midpoint signals is proposed and initial experimental results are also presented. [ABSTRACT FROM AUTHOR]

Photoacoustic image reconstruction algorithms are usually slow due to the large sizes of data that are processed. This paper proposes a method for exact photoacoustic reconstruction for the spherical geometry in the limiting case of a continuous aperture and infinite measurement bandwidth that is faster than existing methods namely (1) backprojection method and (2) the Norton-Linzer method [S. J. Norton and M. Linzer, 'Ultrasonic reflectivity imaging in three dimensions: Exact inverse scattering solution for plane, cylindrical and spherical apertures,' Biomedical Engineering, IEEE Trans. BME 28, 202-220 (1981)]. The initial pressure distribution is expanded using a spherical Fourier Bessel series. The proposed method estimates the Fourier Bessel coefficients and subsequently recovers the pressure distribution. A concept of frequency-radial duality is introduced that separates the information from the different radial basis functions by using frequencies corresponding to the Bessel zeros. This approach provides a means to analyze the information obtained given a measurement bandwidth. Using order analysis and numerical experiments, the proposed method is shown to be faster than both the backprojection and the Norton-Linzer methods. Further, the reconstructed images using the proposed methodology were of similar quality to the Norton-Linzer method and were better than the approximate backprojection method. [ABSTRACT FROM AUTHOR]

Recent published results in inverse scattering generally show the difficulty in dealing with moderate to high contrast inhomogeneities when employing linearized or iteratively linearized algorithms (e.g., distorted Born iterative method). This paper presents a fully nonlinear algorithm utilizing full wave field data, that results in ultrasound computed tomographic images from a laboratory breast scanner, and shows several such unique images from volunteer subjects. The forward problem, data collection process and inverse scattering algorithm used are discussed. A functional that represents the 'best fit' between predicted and measured data is minimized, and therefore requires a very fast forward problem solver, Jacobian calculation, and gradient estimation, all of which are described. The data collection device is described. The algorithm and device yield quantitative estimates of human breast tissue in vivo. Several high resolution images, measuring ∼150 by 150 wavelengths, obtained from the 2D inverse scattering algorithms, using data collected from a first prototype, are shown and discussed. The quantitative values are compared with previous published work. [ABSTRACT FROM AUTHOR]

The effect known as 'weak Anderson localization,' 'coherent backscattering,' or 'enhanced back-scattering' is a physical phenomenon that occurs in random systems, e.g., disordered media and linear wave systems, including reverberation rooms: The mean square response is increased at the drive point. In a reverberation room, this means that one can expect an increase of the reverberant sound field at the position of the source that generates the sound field. This affects the sound power output of the source and is therefore of practical concern. The relative increase of reverberant energy is described by the concentration factor, which is usually assumed to be 2. However, because of the stronger direct sound field at the source position, it is obviously very difficult to measure this quantity directly under steady-state conditions. A related parameter of crucial importance for the ensemble statistics of responses in rooms is the modal kurtosis, which is usually assumed to be 3. The modal kurtosis is also very difficult to measure directly. This paper presents the results of an indirect experimental estimation of the two parameters. [ABSTRACT FROM AUTHOR]

Conventional detection of humpback vocalizations is often based on frequency summation of band-limited spectrograms under the assumption that energy (square of the Fourier amplitude) is the appropriate metric. Power-law detectors allow for a higher power of the Fourier amplitude, appropriate when the signal occupies a limited but unknown subset of these frequencies. Shipping noise is non-stationary and colored and problematic for many marine mammal detection algorithms. Modifications to the standard power-law form are introduced to minimize the effects of this noise. These same modifications also allow for a fixed detection threshold, applicable to broadly varying ocean acoustic environments. The detection algorithm is general enough to detect all types of humpback vocalizations. Tests presented in this paper show this algorithm matches human detection performance with an acceptably small probability of false alarms (PFA < 6%) for even the noisiest environments. The detector outperforms energy detection techniques, providing a probability of detection PD = 95% for PFA < 5% for three acoustic deployments, compared to PFA > 40% for two energy-based techniques. The generalized power-law detector also can be used for basic parameter estimation and can be adapted for other types of transient sounds. [ABSTRACT FROM AUTHOR]

This paper demonstrates an algorithm for computing the instantaneous correlation coefficient between two signals. The algorithm is the computational engine for analyzing the time-varying coordination between signals, which is called correlation map analysis (CMA). Correlation is computed around any pair of points in the two input signals. Thus, coordination can be assessed across a continuous range of temporal offsets and be detected even when changing over time due to temporal fluctuations. The correlation algorithm has two major features: (i) it is structurally similar to a tunable filter, requiring only one parameter to set its cutoff frequency (and sensitivity), (ii) it can be applied either uni-directionally (computing correlation based only on previous samples) or bi-directionally (computing correlation based on both previous and future samples). Computing instantaneous correlation for a range of time offsets between two signals produces a 2D correlation map, in which correlation is characterized as a function of time and temporal offset. Graphic visualization of the correlation map provides rapid assessment of how correspondence patterns progress through time. The utility of the algorithm and of CMA are exemplified using the spatial and temporal coordination of various audible and visible components associated with linguistic performance. [ABSTRACT FROM AUTHOR]

The estimation of doubly spread underwater acoustic channels is addressed. By exploiting the sparsity in the delay-Doppler domain, this paper proposes a fast projected gradient method (FPGM) that can handle complex-valued data for estimating the delay-Doppler spread function of a time-varying channel. The proposed FPGM formulates the sparse channel estimation as a complex-valued convex optimization using an ℓ1-norm constraint. Conventional approaches to complex-valued optimization split the complex variables into their real and imaginary parts; this doubles the dimension compared with the original problem and may break the special data structure. Unlike the conventional methods, the proposed method directly handles the complex variables as a whole without splitting them into real numbers; hence the dimension will not increase. By exploiting the block Toeplitz-like structure of the coefficient matrix, the computational complexity of the FPGM is reduced to O(LNlogN), where L is the dimension of the Doppler shift and N is the signal length. Simulation results verify the accuracy and efficiency of the FPGM, indicating that is robust to parameter selection and is orders-of-magnitude faster than standard convex optimization algorithms. The Kauai experimental data processing results are also provided to demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Many outdoor sound sources, such as aircraft or ground vehicles, exhibit directional radiation patterns. However, long-range sound propagation algorithms are usually formulated for omnidirectional point sources. This paper describes two methods for incorporating directional sources into long-range sound propagation algorithms. The first is the equivalent source method (ESM), which determines a compact distribution of omnidirectional point sources reproducing a given directivity pattern in the far field. This method can be used with any propagation algorithm because it explicitly reconstructs a source function as a set of point sources with certain amplitudes and positions. The second is a directional starter method (DSM), which is developed specifically for the parabolic equation (PE) algorithms. This method derives narrow- or wide-angle directional starter fields, corresponding to a given source directivity pattern, without reconstructing the equivalent source distribution. Although the ESM can also be used for the PE, the DSM is simpler and can be more convenient, especially if the sound propagation is calculated only for one or a few azimuthal directions. While these two methods are found to produce generally distinct starter fields, they nonetheless yield identical directivity patterns. [ABSTRACT FROM AUTHOR]

The problem of locating point-like targets beyond the classical resolution limit is revisited. Although time-reversal MUltiple SIgnal Classification (MUSIC) is known for its super-resolution ability in localization of point scatterers, in the presence of noise this super-resolution property will easily break down. In this paper a phase-coherent version of time-reversal MUSIC is proposed, which can overcome this fundamental limit. The algorithm has been tested employing synthetic multiple scattering data based on the Foldy-Lax model, as well as experimental ultrasound data acquired in a water tank. Using a limited frequency band, it was demonstrated that the phase-coherent MUSIC algorithm has the potential of giving significantly better resolved scatterer locations than standard time-reversal MUSIC. [ABSTRACT FROM AUTHOR]

A method is introduced that uses principal component analysis (PCA) to detect emergent acoustic signals. Emergent signal detection is frequently used in radar applications to detect signals of interest in background clutter and in cognitive radio to detect the primary user in a frequency band. The method presented differs from other standard techniques in that the detection of the signal of interest is accomplished by detecting a change in the covariance between two channels of data instead of detecting the change in statistics of a single channel of data. For this paper, PCA is able to detect emergent acoustic signals by detecting when there is a change in the eigenvalue subspace of the covariance matrix caused by the addition of the signal of interest. The algorithm's performance is compared to an energy detector and the Neyman-Pearson theorem. Acoustic simulations were used to verify the performance of the algorithm. Simulations were also used to examine the effectiveness of the algorithm under various signal-to-interferer and signal-to-noise ratios, and using various test signals. [ABSTRACT FROM AUTHOR]

Estimation of the parameters of a multipath underwater acoustic channel is of great interest for a variety of applications. This paper proposes a high-resolution method for jointly estimating the multipath time delays, Doppler scales, and attenuation amplitudes of a time-varying acoustical channel. The proposed method formulates the estimation of channel parameters into a sparse representation problem. With the ℓ1-norm as the measure of sparsity, the proposed method makes use of the basis pursuit (BP) criterion to find the sparse solution. The ill-conditioning can be effectively reduced by the ℓ1-norm regularization. Unlike many existing methods that are only applicable to narrowband signals, the proposed method can handle both narrowband and wideband signals. Simulation results are provided to verify the performance and effectiveness of the proposed algorithm, indicating that it has a super-resolution in both delay and Doppler domain, and it is robust to noise. [ABSTRACT FROM AUTHOR]

In this paper an algorithm is described for grouping sperm whale clicks received at a single hydrophone into click-trains associated with a given whale and propagation path. To accomplish this, features are extracted from pairs of clicks in order to derive a statistical measure of click similarity. Then, the algorithm maximizes a global measure of similarity between the associated clicks. In addition to grouping clicks according to click-trains, it classifies click-trains into a propagation path (direct, surface, bottom reverberation). The propagation path is identified using a combination of (1) a statistical classifier operating on features extracted from individual clicks and (2) the cross-correlation of click-trains to identify the relative time delays. The algorithm is demonstrated on at-sea recorded data, showing the elimination of time-delay ambiguities associated with the reverberation paths observed at sensor pairs. [ABSTRACT FROM AUTHOR]

Accurate characterization of tissue pathologies using ultrasonic attenuation is strongly dependent on the accuracy of the algorithm that is used to obtain the attenuation coefficient estimates. In this paper, computer simulations were used to compare the accuracy and the precision of the three methods that are commonly used to estimate the local ultrasonic attenuation within a region of interest (ROI) in tissue; namely, the spectral log difference method, the spectral difference method, and the hybrid method. The effects of the inhomgeneities within the ROI on the accuracy of the three algorithms were studied, and the optimal ROI size (the number of independent echoes laterally and the number of pulse lengths axially) was quantified for each method. The three algorithms were tested for when the ROI was homogeneous, the ROI had variations in scatterer number density, and the ROI had variations in effective scatterer size. The results showed that when the ROI was homogeneous, the spectral difference method had the highest accuracy and precision followed by the spectral log difference method and the hybrid method, respectively. Also, when the scatterer number density varied, the spectral difference method completely failed, while the log difference method and hybrid method still gave good results. Lastly, when the scatterer size varied, all of the methods failed. [ABSTRACT FROM AUTHOR]

This paper develops a sequential trans-dimensional Monte Carlo algorithm for geoacoustic inversion in a strongly range-dependent environment. The algorithm applies advanced Markov chain Monte Carlo methods in combination with sequential techniques (particle filters) to carry out geoacoustic inversions for consecutive data sets acquired along a track. Changes in model parametrization along the track (e.g., number of sediment layers) are accounted for with trans-dimensional partition modeling, which intrinsically determines the amount of structure supported by the data information content. Challenging issues of rapid environmental change between consecutive data sets and high information content (peaked likelihood) are addressed by bridging distributions implemented using annealed importance sampling. This provides an efficient method to locate high-likelihood regions for new data which are distant and/or disjoint from previous high-likelihood regions. The algorithm is applied to simulated reflection-coefficient data along a track, such as can be collected using a towed array close to the seabed. The simulated environment varies rapidly along the track, with changes in the number of layers, layer thicknesses, and geoacoustic parameters within layers. In addition, the seabed contains a geologic fault, where all layers are offset abruptly, and an erosional channel. Changes in noise level are also considered. [ABSTRACT FROM AUTHOR]

Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation. [ABSTRACT FROM AUTHOR]

Recently, a computational algorithm based on Bayesian data analysis was presented to invert the linear rheology of branched polymers [Shanbhag, S., Rheol. Acta 49, 411-422 (2010)]. When rheological data of an unknown polymer mixture are supplied, the algorithm produces an exhaustive distribution of structures and compositions, consistent with the rheology. Frequently, it identifies multiple or degenerate structures. In this proof-of-concept paper, a resolution of the degeneracy is sought by appealing to the concept of combinatorial rheology [Larson, R. G., Macromolecules 34, 4556-4571 (2001)], where the unknown sample is strategically blended with a well-characterized fraction. Experimental load is alleviated by identifying the optimal type, molecular weight, and composition of the polymer fraction to blend with the unknown sample, to discriminate between the degenerate structures, most conclusively. Two methods are proposed and tested on a particular mixture that is characterized by severe degeneracy. Both proposed methods are able to correctly identify the structure and composition of the original mixture by requiring two additional rheological experiments. [ABSTRACT FROM AUTHOR]

This paper presents how the shape of a brass instrument can be optimized with respect to its intonation properties. The instrument is modeled using a hybrid method between a lossy one-dimensional transmission line analogy for the slowly flaring part of the instrument, and a two-dimensional finite element model for the rapidly flaring part. The optimization employs gradient-based algorithms, and allows for a large number of design variables. Through the use of an appropriate choice of design variables, the algorithm is capable of rapidly finding horn profiles that are optimal subject to various geometric constraints, such as increasing or convex bell flares. It is found that under a convexity constraint, brass wind bells that are optimal with respect to an intonation condition can be constructed of piecewise conical sections. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Despite using different algorithms, most unsupervised automatic phone segmentation methods achieve similar performance in terms of percentage correct boundary detection. Nevertheless, unsupervised segmentation algorithms are not able to perfectly reproduce manually obtained reference transcriptions. This paper investigates fundamental problems for unsupervised segmentation algorithms by comparing a phone segmentation obtained using only the acoustic information present in the signal with a reference segmentation created by human transcribers. The analyses of the output of an unsupervised speech segmentation method that uses acoustic change to hypothesize boundaries showed that acoustic change is a fairly good indicator of segment boundaries: over two-thirds of the hypothesized boundaries coincide with segment boundaries. Statistical analyses showed that the errors are related to segment duration, sequences of similar segments, and inherently dynamic phones. In order to improve unsupervised automatic speech segmentation, current one-stage bottom-up segmentation methods should be expanded into two-stage segmentation methods that are able to use a mix of bottom-up information extracted from the speech signal and automatically derived top-down information. In this way, unsupervised methods can be improved while remaining flexible and language-independent. [ABSTRACT FROM AUTHOR]

This paper addresses the design of digital fractional-octave-band filters with energy conservation and perfect reconstruction—i.e., whose outputs to each fractional-octave-band correctly sum up to the original signal and whose partial energies at each output correctly sum up to the overall signal energy—a combination of properties that cannot be met by any current design despite its considerable importance in many applications. A solution is devised based on the introduction of complex basis functions that span the outputs of the fractional-octave bands and whose real and imaginary parts form two individually—but not mutually—orthogonal bases. This imposes a “partition-of-unity” condition on the design of the filter frequency gains such that they exactly sum up to one over the frequency axis. The practical implementation of the proposed solution uses the discrete Fourier transform, and a fast algorithm is implemented using the fast Fourier transform. The proposed filters are well suited to any application involving the post-processing of finite-energy signals. They closely match the international standard templates, except for a small departure at the bandedge frequencies which can be made arbitrarily small. [ABSTRACT FROM AUTHOR]

This paper presents an advanced method to acoustically assess animal abundance. The framework combines supervised classification (song-type and individual identity recognition), unsupervised classification (individual identity clustering), and the mark-recapture model of abundance estimation. The underlying algorithm is based on clustering using hidden Markov models (HMMs) and Gaussian mixture models (GMMs) similar to methods used in the speech recognition community for tasks such as speaker identification and clustering. Initial experiments using a Norwegian ortolan bunting (Emberiza hortulana) data set show the feasibility and effectiveness of the approach. Individually distinct acoustic features have been observed in a wide range of animal species, and this combined with the widespread success of speaker identification and verification methods for human speech suggests that robust automatic identification of individuals from their vocalizations is attainable. Only a few studies, however, have yet attempted to use individual acoustic distinctiveness to directly assess population density and structure. The approach introduced here offers a direct mechanism for using individual vocal variability to create simpler and more accurate population assessment tools in vocally active species. [ABSTRACT FROM AUTHOR]

There are varieties of wideband direction-of-arrival (DOA) estimation algorithms. Their structure comprises a number of narrowband ones, each performs in one frequency in a given bandwidth, and then different responses should be combined in a proper way to yield true DOAs. Hence, wideband algorithms are always complex and so non-real-time. This paper investigates a method to derive a flat response of narrowband multiple signal classification (MUSIC) [R. O. Schmidt, IEEE Trans. Antennas Propag., 34, 276–280 (1986)] algorithm in the whole frequencies of given band. Therefore, required conditions of applying narrowband algorithm on wideband impinging signals will be given through a concrete analysis. It could be found out that array sensor locations are able to compensate the frequency variations to reach a flat response of DOAs in a specified wideband frequency. [ABSTRACT FROM AUTHOR]

This paper develops an approach to three-dimensional source tracking in an uncertain ocean environment using a horizontal line array (HLA). The tracking algorithm combines matched-field focalization for environmental (seabed and water column) and source-bearing model parameters with the Viterbi algorithm for range-depth estimation and includes physical constraints on source velocity. The ability to track a source despite environmental uncertainty is examined using synthetic test cases for various track geometries and with varying degrees of prior information for environmental parameters. Performance is evaluated for a range of signal-to-noise ratios in terms of the probability of estimating a track within acceptable position/depth errors. The algorithm substantially outperforms tracking with poor environmental estimates and generally obtains results close to those obtained with exact environmental knowledge. The approach is also applied to measured narrowband data recorded on a bottom-moored HLA in shallow water (the Barents Sea) and shown to successfully track both a towed submerged source and a surface ship in cases where simpler tracking algorithms failed. [ABSTRACT FROM AUTHOR]