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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 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]

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]

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]

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 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]

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]

Gas-assisted etching and deposition with focused ion beams are unique and flexible methods for the fabrication of nanostructures. To understand and improve these processes the ability to accurately simulate and predict the resulting structures is very important. In this paper we present a nonlocal recoil-based algorithm for topography simulation of ion-beam induced gas-assisted deposition. We have fabricated flying roof like overhanging structures and found very good agreement between simulation and experiment. These structures cannot be explained with a local model. Furthermore, we demonstrate a considerable influence of the beam diameter on the resulting structure by comparing otherwise identical simulations with different beam diameter. [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]

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]

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 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]

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]

To avoid the requirement set by standard near-field acoustical holography (NAH) to measure an area that fully covers the source, a set of so-called patch NAH methods has been introduced. One such method is the statistically optimized NAH (SONAH). In this method, the acoustic quantities on a mapping surface near the measurement surface are calculated by using a transfer matrix defined in such a way that all propagating waves and a weighted set of evanescent waves are projected with optimal average accuracy. The present paper gives an overview of the basic theory of SONAH, including a description of phenomena such as spatial aliasing and wave-number domain leakage. A revised and generalized mathematical formulation is given, covering the calculation of all three components of particle velocity and the use of up to six virtual source planes. A set of formulas for the inherent estimation error level of the method is derived and used to visualize the regions of validity of the SONAH predictions for some typical microphone array geometries. The sensitivity of the inherent error level distribution to changes in the parameters of the SONAH algorithm is also investigated. [ABSTRACT FROM AUTHOR]

This paper compares focalization and marginalization approaches to source tracking when uncertain ocean environmental parameters are included, in addition to source locations, in a Bayesian inversion formulation. Focalization consists of determining the source track that maximizes the posterior probability density (PPD) over all source and environmental parameters. An efficient focalization approach is developed by applying the Viterbi algorithm to compute the optimal track from range-depth conditional probability distributions for each realization of the environmental parameters. This allows source locations to be treated implicitly and the optimization to be applied only to environmental parameters, substantially reducing the dimensionality and complexity of the problem. Marginalization consists of first integrating the PPD over the environmental unknowns to obtain a sequence of joint marginal probability distributions over source range and depth along the track. Applying the Viterbi algorithm to these marginal distributions defines the track estimate, and the distributions themselves quantify the track uncertainty. Monte Carlo analysis of the two approaches for a test case involving both geoacoustic and water-column uncertainties indicates that marginalization provides a significantly more reliable approach to tracking in an unknown environment. [ABSTRACT FROM AUTHOR]

This paper evaluates speech enhancement in binaural multimicrophone hearing aids by noise reduction algorithms based on the multichannel Wiener filter (MWF) and the MWF with partial noise estimate (MWF-N). Both algorithms are specifically developed to combine noise reduction with the preservation of binaural cues. Objective and perceptual evaluations were performed with different speech-in-multitalker-babble configurations in two different acoustic environments. The main conclusions are as follows: (a) A bilateral MWF with perfect voice activity detection equals or outperforms a bilateral adaptive directional microphone in terms of speech enhancement while preserving the binaural cues of the speech component. (b) A significant gain in speech enhancement is found when transmitting one contralateral microphone signal to the MWF active at the ipsilateral hearing aid. Adding a second contralateral microphone showed a significant improvement during the objective evaluations but not in the subset of scenarios tested during the perceptual evaluations. (c) Adding the partial noise estimate to the MWF, done to improve the spatial awareness of the hearing aid user, reduces the amount of speech enhancement in a limited way. In some conditions the MWF-N even outperformed the MWF possibly due to an improved spatial release from masking. [ABSTRACT FROM AUTHOR]

This paper focuses on the importance of source speaker selection for a weighted codebook mapping based voice conversion algorithm. First, the dependency on source speakers is evaluated in a subjective listening test using 180 different source-target pairs from a database of 20 speakers. Subjective scores for similarity to target speaker’s voice and quality are obtained. Statistical analysis of scores confirms the dependence of performance on source speakers for both male-to-male and female-to-female transformations. A source speaker selection algorithm is devised given a target speaker and a set of source speaker candidates. For this purpose, an artificial neural network (ANN) is trained that learns the regression between a set of acoustical distance measures and the subjective scores. The estimated scores are used in source speaker ranking. The average cross-correlation coefficient between rankings obtained from median subjective scores and rankings estimated by the algorithm is 0.84 for similarity and 0.78 for quality in male-to-male transformations. The results for female-to-female transformations were less reliable with a cross-correlation value of 0.58 for both similarity and quality. [ABSTRACT FROM AUTHOR]

An algorithm allowing simultaneous detection and localization of multiple submerged targets crossing an acoustic tripwire based on forward scattering is described and then evaluated based upon data collected at sea. This paper quantifies the agreement between the theoretical performance and the results obtained from processing data gathered at sea for crossings at several depths and ranges. Targets crossing the acoustic field produce shadows on each side of the barrier, for specific sensors and for specific acoustic paths. In post-processing, a model is invoked to associate expected paths with the observed shadows. This process allows triangulation of the target’s position inside the acoustic field. Precise localization is achieved by taking advantage of the multipath propagation structure of the received signal, together with the diversity of the source and receiver locations. Environmental robustness is demonstrated using simulations and can be explained by the use of an array of sources spatially distributed through the water column. [ABSTRACT FROM AUTHOR]

Noise fields encountered in real-life scenarios can often be approximated as spherical or cylindrical noise fields. The characteristics of the noise field can be described by a spatial coherence function. For simulation purposes, researchers in the signal processing community often require sensor signals that exhibit a specific spatial coherence function. In addition, they often require a specific type of noise such as temporally correlated noise, babble speech that comprises a mixture of mutually independent speech fragments, or factory noise. Existing algorithms are unable to generate sensor signals such as babble speech and factory noise observed in an arbitrary noise field. In this paper an efficient algorithm is developed that generates multisensor signals under a predefined spatial coherence constraint. The benefit of the developed algorithm is twofold. Firstly, there are no restrictions on the spatial coherence function. Secondly, to generate M sensor signals the algorithm requires only M mutually independent noise signals. The performance evaluation shows that the developed algorithm is able to generate a more accurate spatial coherence between the generated sensor signals compared to the so-called image method that is frequently used in the signal processing community. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of facial motion during speech to identify linearly independent kinematic regions. The data consists of three-dimensional displacement records of a set of markers located on a subject’s face while producing speech. A QR factorization with column pivoting algorithm selects a subset of markers with independent motion patterns. The subset is used as a basis to fit the motion of the other facial markers, which determines facial regions of influence of each of the linearly independent markers. Those regions constitute kinematic “eigenregions” whose combined motion produces the total motion of the face. Facial animations may be generated by driving the independent markers with collected displacement records. [ABSTRACT FROM AUTHOR]

In this paper, real-time results are given for broadband multichannel active noise control using the regularized modified filtered-error algorithm. As compared to the standard filtered-error algorithm, the improved convergence rate and stability of the algorithm are obtained by using an inner–outer factorization of the transfer path between the actuators and the error sensors, combined with a delay compensation technique using double control filters and a regularization technique that preserves the factorization properties. The latter techniques allow the use of relatively simple and efficient adaptation schemes in which filtering of the reference signals is unnecessary. Results are given for a multichannel adaptive feedback implementation based on the internal model control principle. In feedforward systems based on this algorithm, colored reference signals may lead to reduced convergence rates. An adaptive extension based on the use of affine projections is presented, for which real-time results and simulations are given, showing the improved convergence rates of the regularized modified filtered-error algorithm for colored reference signals. [ABSTRACT FROM AUTHOR]

In this paper, a systematic treatment for developing a noise removal system based on the fundamental principle of reinforcement learning and fuzzy cerebellar model articulation controller (FCMAC) is presented. The proposed system improves its performance over time through two mechanisms. First, the modified stochastic real-valued algorithm, learning from its own mistakes via the reinforcement signal and reinforcing its action to improve future performance, is used for searching the optimal noise spectrum for the overall training system. Second, system states associated with the positive reinforcement are memorized by FCMAC-based neurons, where, in the future, similar states will share the experiences already stored there and then lead the action to a more positive situation. In this work, FCMAC’s intrinsically poor approximation of rapidly varying functions is solved by taking the complex semicepstrum. In addition, the FCMAC provides an improvement in accuracy of function approximation without losing the property of generalization, which makes the high fidelity digital signal processing possible. [ABSTRACT FROM AUTHOR]

Passive time reversal has aroused considerable interest in underwater communications as a computationally inexpensive means of mitigating the intersymbol interference introduced by the channel using a receiver array. In this paper the basic technique is extended by adaptively weighting sensor contributions to partially compensate for degraded focusing due to mismatch between the assumed and actual medium impulse responses. Two algorithms are proposed, one of which restores constructive interference between sensors, and the other one minimizes the output residual as in widely used equalization schemes. These are compared with plain time reversal and variants that employ postequalization and channel tracking. They are shown to improve the residual error and temporal stability of basic time reversal with very little added complexity. Results are presented for data collected in a passive time-reversal experiment that was conducted during the MREA’04 sea trial. In that experiment a single acoustic projector generated a 2/4-PSK (phase-shift keyed) stream at 200/400 baud, modulated at 3.6 kHz, and received at a range of about 2 km on a sparse vertical array with eight hydrophones. The data were found to exhibit significant Doppler scaling, and a resampling-based preprocessing method is also proposed here to compensate for that scaling. [ABSTRACT FROM AUTHOR]

A new frequency-domain approach to photoacoustic tomography has recently been proposed, promising to overcome some of the shortcomings associated with the pulsed photoacoustic approach. This approach offers many of the benefits of pulsed photoacoustics but requires a different set of equations for modeling of the forward and inverse problems due to the longer time scales involved in the optical input signal. The theory of photoacoustic tomography with an optical input that is not necessarily a short pulse is considered in this paper. The full optical, thermal, and acoustic governing equations are derived. A transfer function approach is taken for the solution and analysis of this problem. The results and implications are compared with those of pulsed photoacoustics and traditional ultrasonic diffraction tomography. A Fourier diffraction theorem is also presented, which could be used as a basis for the development of tomographic imaging algorithms. [ABSTRACT FROM AUTHOR]

Using microphone arrays for estimating source locations and strengths has become common practice in aeroacoustic applications. The classical delay-and-sum approach suffers from low resolution and high sidelobes and the resulting beamforming maps are difficult to interpret. The deconvolution approach for the mapping of acoustic sources (DAMAS) deconvolution algorithm recovers the actual source levels from the contaminated delay-and-sum results by defining an inverse problem that can be represented as a linear system of equations. In this paper, the deconvolution problem is carried onto the sparse signal representation area and a sparsity constrained deconvolution approach (SC-DAMAS) is presented for solving the DAMAS inverse problem. A sparsity preserving covariance matrix fitting approach (CMF) is also presented to overcome the drawbacks of the DAMAS inverse problem. The proposed algorithms are convex optimization problems. Our simulations show that CMF and SC-DAMAS outperform DAMAS and as the noise in the measurements increases, CMF works better than both DAMAS and SC-DAMAS. It is observed that the proposed algorithms converge faster than DAMAS. A modification to SC-DAMAS is also provided which makes it significantly faster than DAMAS and CMF. For the correlated source case, the CMF-C algorithm is proposed and compared with DAMAS-C. Improvements in performance are obtained similar to the uncorrelated case. [ABSTRACT FROM AUTHOR]

The paper describes a general approach for processing data from a guided wave transducer array on a plate-like structure. The raw data set from such an array contains time-domain signals from each transmitter–receiver combination. The technique is based on linear superposition of signals in the frequency domain with some amplitude and phase factors and can be applied to any array geometry and any types of array elements. The problem of finding optimal coefficients, which allow the best resolution to be achieved with the minimum number of array elements, is investigated. It is shown that improvements in resolution are obtained at the expense of sensitivity to noise. A method of quantifying this sensitivity is presented. Results are shown that illustrate the application of the technique to a linear array and an array of circular geometry (containing a single ring of elements). Experimental data obtained from a guided wave array containing electromagnetic acoustic transducer elements for exciting and detecting the S0 Lamb wave mode in a 5-mm-thick aluminum plate are processed with different algorithms and the results are discussed. Generalization of the technique for the case of multimode media is suggested. [ABSTRACT FROM AUTHOR]

Researchers in the signal processing community often require sensor signals that result from a spherically or cylindrically isotropic noise field for simulation purposes. Although it has been shown that these signals can be generated using a number of uncorrelated noise sources that are uniformly spaced on a sphere or cylinder, this method is seldom used in practice. In this paper algorithms that generate sensor signals of an arbitrary one- and three-dimensional array that result from a spherically or cylindrically isotropic noise field are developed. Furthermore, the influence of the number of noise sources on the accuracy of the generated sensor signals is investigated. [ABSTRACT FROM AUTHOR]

Examining phonetic categorization in multidimensional stimulus spaces poses a number of practical problems. The traditional method of forced identification becomes prohibitive when the number and size of stimulus dimensions becomes increasingly large. In response, Evans and Iverson [J. Acoust. Soc. Am. 115, 352–361 (2004)] proposed an adaptive tracking algorithm for finding vowel best exemplars in a multidimensional space. This algorithm converged on best exemplars in a small number of trials; however, the search method was designed explicitly for vowel stimuli. In this paper, a more general multidimensional search algorithm is described, and results from simulations and experiments using the proposed algorithm are presented. [ABSTRACT FROM AUTHOR]

This paper addresses the task of recovering the geoacoustic parameters of a shallow-water environment using measurements of the acoustic field due to a known source and a neural network based inversion process. First, a novel efficient “observable” of the acoustic signal is proposed, which represents the signal in accordance with the recoverable parameters. Motivated by recent studies in non-Gaussian statistical theory, the observable is defined as a set of estimated model parameters of the alpha-stable distributions, which fit the marginal statistics of the wavelet subband coefficients, obtained after the transformation of the original signal via a one-dimensional wavelet decomposition. Following the modeling process to extract the observables as features, a radial basis functions neural network is employed to approximate the vector function that takes as input the observables and gives as output the corresponding set of environmental parameters. The performance of the proposed approach in recovering the sound speed and density in the substrate of a typical shallow-water environment is evaluated using a database of synthetic acoustic signals, generated by means of a normal-mode acoustic propagation algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an investigation of the reconstruction of sound field parameters close to the surface of arbitrarily shaped sound sources. The field is reconstructed using nearfield acoustical holography (NAH) in spherical coordinates. Of particular interest are source shapes where the Rayleigh hypothesis is violated. To overcome the limitation of the minimal sphere given by the validity restriction of the Rayleigh hypothesis an algorithm is proposed for extracting local information from the nonconvergent NAH solution. For the assessment of the results an appropriate virtual test rig is developed employing the Kirchhoff–Helmholtz integral theorem. [ABSTRACT FROM AUTHOR]

This paper describes the main features of the sustain-pedal effect in the piano through signal analysis and presents an algorithm for simulating the effect. The sustain pedal is found to increase the decay time of partials in the middle range of the keyboard, but this effect is not observed in the case of the bass and treble tones. The amplitude beating characteristics of piano tones are measured with and without the sustain pedal engaged, and amplitude envelopes of partial overtone decay are estimated and displayed. It is found that the usage of the sustain pedal introduces interesting distortions of the two-stage decay. The string register response was investigated by removing partials from recorded tones; it was observed that as the string register is free to vibrate, the amount of sympathetic vibrations is increased. The synthesis algorithm, which simulates the string register, is based on 12 string models that correspond to the lowest tones of the piano. The algorithm has been tested with recorded piano tones without the sustain pedal. The objective and subjective results show that the algorithm is able to approximately reproduce the main features of the sustain-pedal effect. [ABSTRACT FROM AUTHOR]

This paper presents a high-order accelerated algorithm for the solution of the integral-equation formulation of volumetric scattering problems. The scheme is particularly well suited to the analysis of “thin” structures as they arise in certain applications (e.g., material coatings); in addition, it is also designed to be used in conjunction with existing low-order FFT-based codes to upgrade their order of accuracy through a suitable treatment of material interfaces. The high-order convergence of the new procedure is attained through a combination of changes of parametric variables (to resolve the singularities of the Green function) and “partitions of unity” (to allow for a simple implementation of spectrally accurate quadratures away from singular points). Accelerated evaluations of the interaction between degrees of freedom, on the other hand, are accomplished by incorporating (two-face) equivalent source approximations on Cartesian grids. A detailed account of the main algorithmic components of the scheme are presented, together with a brief review of the corresponding error and performance analyses which are exemplified with a variety of numerical results. [ABSTRACT FROM AUTHOR]

The propagation of noise from the data to the reconstructed speed of sound image by inverse scattering within the framework of the Lippmann-Schwinger integral equation of scattering is discussed. The inversion algorithm that was used consisted in minimizing a Tikhonov functional in the unknown speed of sound. The gradient of the objective functional was computed by the method of the adjoint fields. An analytical expression for the inverse scattering covariance matrix of the image noise was derived. It was shown that the covariance matrix in the linear x-ray computed tomography is a special case of the inverse scattering matrix derived in this paper. The matrix was also analyzed in the limit of the linearized Born approximation, and the results were found to be in qualitative agreement with those recently reported in the literature for Born inversion using filtered backpropagation algorithm. Finally, the applicability of the analysis reported here to the obstacle problem and the physical optics approximation was discussed. [ABSTRACT FROM AUTHOR]

Two new experiments were created to characterize the elasticity of soft tissue using sonoelastography. In both experiments the spectral variance image displayed on a GE LOGIC 700 ultrasound machine shows a moving interference pattern that travels at a very small fraction of the shear wave speed. The goal of this paper is to devise and test algorithms to calculate the speed of the moving interference pattern using the arrival times of these same patterns. A geometric optics expansion is used to obtain Eikonal equations relating the moving interference pattern arrival times to the moving interference pattern speed and then to the shear wave speed. A cross-correlation procedure is employed to find the arrival times; and an inverse Eikonal solver called the level curve method computes the speed of the interference pattern. The algorithm is tested on data from a phantom experiment performed at the University of Rochester Center for Biomedical Ultrasound. [ABSTRACT FROM AUTHOR]

Although many technologies exist for inspecting piping systems, they are most successful on straight pipes and are often unable to accommodate the added complexities of pipe elbows, bends, twists, and branches, particularly if the region of interest is inaccessible. This paper presents a numerical technique based on the elastodynamic finite integration technique for simulating guided elastic wave propagation in piping systems. Comparisons show agreement between experimental and simulated data, and guided wave interaction with flaws, focusing, and propagation in pipe bends are presented. These examples demonstrate the ability of the simulation method to be used to study elastic wave propagation in piping systems which include three-dimensional pipe bends, and suggest its potential as a design tool for designing pipe inspection hardware and ultrasonic signal processing algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a model-based sound synthesis algorithm for the Chinese plucked string instrument called the guqin. The instrument is fretless, which enables smooth pitch glides from one note to another. A version of the digital waveguide synthesis approach is used, where the string length is time-varying and its energy is scaled properly. A body model filter is placed in cascade with the string model. Flageolet tones are synthesized with the so-called ripple filter structure, which is an FIR comb filter in the delay line of a digital waveguide model. In addition, signal analysis of recorded guqin tones is presented. Friction noise produced by gliding the finger across the soundboard has a harmonic structure and is proportional to the gliding speed. For pressed tones, one end of a vibrating string is terminated either by the nail of the thumb or a fingertip. The tones terminated with a fingertip decay faster than those terminated with a thumb. Guqin tones are slightly inharmonic and they exhibit phantom partials. The synthesis model takes into account these characteristic features of the instrument and is able to reproduce them. The synthesis model will be used for rule based synthesis of guqin music. [ABSTRACT FROM AUTHOR]

The ocean is often a complex multipath channel and progress has been made in developing equalization algorithms to overcome this. Unfortunately, many of these algorithms are computationally demanding and not as power-efficient as one would like; in many applications it may be better to trade bit rate for longer operational life. In 2000 the U.S. Navy was developing an underwater wireless acoustic network called Seaweb, for which a number of modulation schemes were being tested in a series of SignalEx experiments. This paper discusses two modulation schemes and associated receiver algorithms that were developed and tested for Seaweb applications. These receiver designs take advantage of time reversal (phase conjugation) and properties of spread spectrum sequences known as Gold sequences. Furthermore, they are much less complex than receivers using adaptive equalizers. This paper will present results of testing these signaling and receiver concepts during two experiments at sea. [ABSTRACT FROM AUTHOR]

Filters are commonly used in sound reproduction and communication systems as a means of compensating for the response of the electro-acoustic plant. Two commonly used filter designs in the field of acoustics are the time reversal filter and the Tikhonov inverse filter. In this paper the influence of transducer sensitivities on the performance of these filters is examined. It is shown that the sensitivity of the transducers can negatively affect the performance of the resulting filter. To compensate for the decrease in performance, diagonal preconditioning can be implemented in the system. It is shown that by using diagonal matrices, which minimize the condition number of the system, the loss in performance arising from unbalanced sensitivities is minimized. This paper proposes an algorithm to find such a set of diagonal matrices and results are presented showing the improvements in performance arising from the modified filter design. [ABSTRACT FROM AUTHOR]

The treatment of time-reversal imaging of multiply scattering point targets developed by the present authors in Gruber et al. [“Time-reversal imaging with multiple signal classification considering multiple scattering between the targets,” J. Acoust. Soc. Am., 115, 3042–3047 (2004)] is reformulated and extended to the estimation of the target scattering strengths using the Foldy–Lax multiple scattering model. It is shown that the time-reversal multiple signal classification (MUSIC) pseudospectrum computed using the background Green function as the steering vector yields accurate estimates of the target locations, even in the presence of strong multiple scattering between the targets, and that the target scattering strengths are readily computed from the so-determined target locations using a nonlinear iterative algorithm. The paper includes computer simulations illustrating the theory and algorithms presented in the paper. [ABSTRACT FROM AUTHOR]

Intravascular ultrasound (IVUS) is known to be the reference tool for preoperative vessel lesion assessments and for endovascular therapy planning. Nevertheless, IVUS echograms only provide subjective information about vessel wall lesions. Since changes in the vascular tissue stiffness are characteristic of vessel pathologies, catheter-based endovascular ultrasound elastography (EVE) has been proposed in the literature as a method for outlining the elastic properties of vessel walls. In this paper, the Lagrangian Speckle Model Estimator (LSME) is formulated for investigations in EVE, i.e., using a polar coordinate system. The method was implemented through an adapted version of the Levenberg-Marquardt minimization algorithm, using the optical flow equations to compute the Jacobbian matrix. The theoretical framework was validated with simulated ultrasound rf data of mechanically complex vessel wall pathologies. The results, corroborated with Ansys finite element software, demonstrated the potential of EVE to provide useful information about the heterogeneous nature of atherosclerotic plaques. [ABSTRACT FROM AUTHOR]

Word frequency in a document has often been utilized in text searching and summarization. Similarly, identifying frequent words or phrases in a speech data set for searching and summarization would also be meaningful. However, obtaining word frequency in a speech data set is difficult, because frequent words are often special terms in the speech and cannot be recognized by a general speech recognizer. This paper proposes another approach that is effective for automatic extraction of such frequent word sections in a speech data set. The proposed method is applicable to any domain of monologue speech, because no language models or specific terms are required in advance. The extracted sections can be regarded as speech labels of some kind or a digest of the speech presentation. The frequent word sections are determined by detecting similar sections, which are sections of audio data that represent the same word or phrase. The similar sections are detected by an efficient algorithm, called Shift Continuous Dynamic Programming (Shift CDP), which realizes fast matching between arbitrary sections in the reference speech pattern and those in the input speech, and enables frame-synchronous extraction of similar sections. In experiments, the algorithm is applied to extract the repeated sections in oral presentation speeches recorded in academic conferences in Japan. The results show that Shift CDP successfully detects similar sections and identifies the frequent word sections in individual presentation speeches, without prior domain knowledge, such as language models and terms. [ABSTRACT FROM AUTHOR]

This paper explores acoustical (or time-dependent) radiosity—a geometrical-acoustics sound-field prediction method that assumes diffuse surface reflection. The literature of acoustical radiosity is briefly reviewed and the advantages and disadvantages of the method are discussed. A discrete form of the integral equation that results from meshing the enclosure boundaries into patches is presented and used in a discrete-time algorithm. Furthermore, an averaging technique is used to reduce computational requirements. To generalize to nonrectangular rooms, a spherical-triangle method is proposed as a means of evaluating the integrals over solid angles that appear in the discrete form of the integral equation. The evaluation of form factors, which also appear in the numerical solution, is discussed for rectangular and nonrectangular rooms. This algorithm and associated methods are validated by comparison of the steady-state predictions for a spherical enclosure to analytical solutions. [ABSTRACT FROM AUTHOR]

The performance of passive localization algorithms can become severely degraded when the target of interest is in the presence of interferers. In this paper, the eigencomponent association (ECA) method of adaptive interference suppression is presented for signals received on horizontal arrays. ECA uses an eigendecomposition to decompose the cross-spectral density matrix (CSDM) of the data and then beamforms each of the eigenvectors. Using an estimate of the target's bearing, the target-to-interference power in each eigenvector at each CSDM update is computed to determine which are dominated by interference. Eigenvectors identified to contain low target-to-interference power are subtracted from the CSDM to suppress the interference. Using this approach, ECA is able to rapidly adapt to the hierarchical swapping of target and interference-related eigenvectors due to relative signal power fluctuations and target dynamics. Simulated data examples consisting of a target and two interferers are presented to demonstrate the effectiveness of ECA. These examples show ECA enabling accurate localization estimates in the presence of interferers, which without using the technique was not possible. [ABSTRACT FROM AUTHOR]

This paper presents an original way to perform wave number inversion from simulated data obtained in a noisy shallow-water environment. In the studied configuration an acoustic source is horizontally towed with respect to a vertical hydrophone array. The inversion is achieved from the combination of three ingredients. First, a modified version of the Prony algorithm is presented and numerical comparison is made to another high-resolution wave number inversion algorithm based on the matrix-pencil technique. Second, knowing that these high-resolution algorithms are classically sensitive to noise, the use of a holographic array processing enables improvement of the signal-to-noise ratio before the inversion is performed. Last, particular care is taken in the representations of the solutions in the wave number space to improve resolution without suffering from aliasing. The dependence of this wave number inversion algorithm on the relevant parameters of the problem is discussed. [ABSTRACT FROM AUTHOR]

Fundamental frequency (F0) extraction is often used in voice quality analysis. In pathological voices with a high degree of instability in F0, it is common for F0 extraction algorithms to fail. In such cases, the faulty F0 values might spoil the possibilities for further data analysis. This paper presents the correlogram, a new method of displaying periodicity. The correlogram is based on the waveform-matching techniques often used in F0 extraction programs, but with no mechanism to select an actual F0 value. Instead, several candidates for F0 are shown as dark bands. The result is presented as a 3D plot with time on the x axis, correlation delay inverted to frequency on the y axis, and correlation on the z axis. The z axis is represented in a gray scale as in a spectrogram. Delays corresponding to integer multiples of the period time will receive high correlation, thus resulting in candidates at F0, F0/2, F0/3, etc. While the correlogram adds little to F0 analysis of normal voices, it is useful for analysis of pathological voices since it illustrates the full complexity of the periodicity in the voice signal. Also, in combination with manual tracing, the correlogram can be used for semimanual F0 extraction. If so, F0 extraction can be performed on many voices that cause problems for conventional F0 extractors. To demonstrate the properties of the method it is applied to synthetic and natural voices, among them six pathological voices, which are characterized by roughness, vocal fry, gratings/scrape, hypofunctional breathiness and voice breaks, or combinations of these. [ABSTRACT FROM AUTHOR]