Multiple Acoustic Sources Research Articles

Multiview Soundfield Imaging in the Projective Ray Space

A soundfield image a data structure that efficiently encodes and represents the wave field captured by a microphone array. Its representation based on the directional plenacoustic function, which defined the radiance of the acoustic paths (rays) that cross the segment that the array lies upon. The soundfield image can be processed as is to develop a variety of applications. In its original formulation, the soundfield image based on a Euclidean parameterization that can accommodate a limited range of rays and suitable for managing a single array only. In this paper, we generalize this methodology to the case of multiple microphone arrays deployed in space. The use of multiple arrays allows us to capture truly global information on the sound field but requires us to rethink the ray space, and adopt a global representation of the acoustic rays based on projective geometry. After introducing the new parameterization, we present two examples of applications: the estimation of the mutual poses of two or more arrays (self-calibration); and the localization of multiple acoustic sources. The effectiveness of these applications proven through simulations well real data experiments.

A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network

Different centralized approaches such as least-squares (LS) and particle filtering (PF) algorithms have been developed to localize an acoustic source by using a distributed acoustic vector sensor (AVS) array. However, such algorithms are either not applicable for multiple sources or rely heavily on sensor-processor communication. In this paper, a distributed unscented PF (DUPF) approach is proposed for multiple acoustic source tracking. At each distributed AVS node, the first-order and the second-order statistics of the local state are estimated by using an unscented information filter (UIF) based PF. The UIF is employed to approximate the optimum importance function due to its simplicity, by which the matrix operation is the state information matrix rather than the covariance matrix of the measurement sequence. These local statistics are then fused between neighbor nodes and a consensus filter is applied to achieve a global estimation. In such an architecture, only the state statistics need to be transmitted among the neighbor nodes. Consequently, the communication cost can be reduced. The distributed posterior Cramér–Rao bound is also derived. Simulation results show that the performance of the DUPF tracking approach is similar to that of centralized PF algorithm and significantly better than that of LS algorithms.

Multiple Wideband Acoustic Source Tracking in 3-D Space Using a Distributed Acoustic Vector Sensor Array

This paper considers the problem of tracking multiple acoustic sources in 3-D space using a distributed acoustic vector sensor array. Unlike the existing two-stage localization approach, which estimates the direction of arrival of the source at each sensor first and then triangulate a 3-D position, a particle filtering approach is developed to directly fuse the signals collected from distributed sensors. To enhance the tracking performance and constrain the computational complexity, an information filter is developed to approximate the optimal importance sampling. Since the position state of the source is linear with the velocity state, a Rao-Blackwellization step is employed to marginalize out the velocity component. In addition, the posterior Cramer-Rao bound is developed to provide a lower performance bound for the distributed tracking system. Both the numerical study and simulations show that the proposed tracking approach significantly outperforms the two-stage localization approaches for 3-D position estimation.

Acoustic source tracking in long baseline microphone arrays

Long baseline microphone arrays, where the distance between single microphones is large, is a cost-effective method to monitor activity in a large region of interest. The high range between source and sensors in a long baseline setup, generally leads to low signal-to-noise ratios (SNR). This affect the performance of localization. This paper investigates how energy based localization (EBL) can be used for localization and tracking of both single and multiple acoustic sources, within a long baseline array. Least-Squares (LS) optimization have been used for EBL-based localization, however the localization performance is sensitive to low SNR. We propose a tracking scheme based on a cost reference particle filter (CRPF) to increase performance during low SNR. The CRPF is a new class of particle filters which is able to estimate the system state from the available observations without a priori knowledge of any probability density function. We present a modified cost function, for EBL based localization, which is incorporated in the CRPF framework to increase tracking performance during simulated wind gusts and background noise. The proposed method outperforms LS based localization in the case of low SNR.

Three-dimensional localization of multiple acoustic sources in shallow ocean with non-Gaussian noise

In this paper, a low-complexity algorithm SAGE-USL is presented for 3-dimensional (3-D) localization of multiple acoustic sources in a shallow ocean with non-Gaussian ambient noise, using a vertical and a horizontal linear array of sensors. In the proposed method, noise is modeled as a Gaussian mixture. Initial estimates of the unknown parameters (source coordinates, signal waveforms and noise parameters) are obtained by known/conventional methods, and a generalized expectation maximization algorithm is used to update the initial estimates iteratively. Simulation results indicate that convergence is reached in a small number of (≤10) iterations. Initialization requires one 2-D search and one 1-D search, and the iterative updates require a sequence of 1-D searches. Therefore the computational complexity of the SAGE-USL algorithm is lower than that of conventional techniques such as 3-D MUSIC by several orders of magnitude. We also derive the Cramér–Rao Bound (CRB) for 3-D localization of multiple sources in a range-independent ocean. Simulation results are presented to show that the root-mean-square localization errors of SAGE-USL are close to the corresponding CRBs and significantly lower than those of 3-D MUSIC.

Acoustic emission localization in beams based on time reversed dispersion

The common approach for the localization of acoustic emission sources in beams requires at least two measurements at different positions on the structure. The acoustic emission event is then located by evaluating the difference of the arrival times of the elastic waves. Here a new method is introduced, which allows the detection and localization of multiple acoustic emission sources with only a single, one point, unidirectional measurement. The method makes use of the time reversal principle and the dispersive behavior of the flexural wave mode. Whereas time-of-arrival (TOA) methods struggle with the distortion of elastic waves due to phase dispersion, the method presented uses the dispersive behavior of guided waves to locate the origin of the acoustic emission event. Therefore, the localization algorithm depends solely on the measured wave form and not on arrival time estimation. The method combines an acoustic emission experiment with a numerical simulation, in which the measured and time reversed displacement history is set as the boundary condition.In this paper, the method is described in detail and the feasibility is experimentally demonstrated by breaking pencil leads on aluminum beams and pultruded carbon fiber reinforced plastic beams according to ASTM E976 (Hsu–Nielsen source). It will be shown, that acoustic emissions are successfully localized even on anisotropic structures and in the case of geometrical complexities such as notches, which lead to reflections, and cross sectional changes, which affect the wave speed. The overall relative error in localizing the acoustic emission sources was found to be below 5%.

Objective measures of blind source separation and the intelligibility of separated speech

When multiple acoustic sources are present in an environment monitored by multiple microphones, each microphone’s response signal is typically a weighted and delayed mixture of the active source signals. Subject to important constraints, these mixtures can be processed using Blind Source Separation (BSS) methods to construct output signals representative of each of the “hidden” acoustic sources in isolation. An important objective measure of the quality of a given “separation solution” is the amount of mutual information remaining in the BSS output channels: the less mutual information across channels, the better the separation. Often, the acoustic signals of interest in a complex acoustic environment are speech sources, and the measure of separation quality that really matters to listeners is speech intelligibility—a subjective (perceptual) measure that cannot be directly computed by a signal processing system. Thus, it would be useful to know how well objective measures of separation quality, such as mutual information, correlate with intelligibility. We present results of perceptual and objective tests exploring the relationship between objective separation metrics and word intelligibility as estimated by listeners’ responses in word identification tasks, for target-word-in-carrier-phrase utterances recorded in an acoustic environment with masking speech babble and broadband noise sources.

Editorial [special issue introduction

In this special issue, ten selected quality papers are presented to address a variety of emerging topics. The multi-input- multi-output (MIMO) radar imaging system is explored in the sensor networks for the surveillance purpose. A new cognitive radio technology that concatenates the available narrow-band white spaces to increase the precision of the range estimation system is addressed. A pseudogradient algorithm is proposed for navigation of autonomous mobile robots operating in unknown environments. The theoretical performance bounds for orthogonal frequency-division multiplexing (OFDM) ranging systems operating in multipath channels are derived subject to Fisher information analysis. An extensive measurement campaign is presented, where data from heterogeneous sensors are collected simultaneously to characterize the diversity of navigation systems in real environments. A new measure for non-line-of-sight identification is demonstrated for wireless localization systems built upon the MIMO-OFDM technology. The approximate maximum likelihood algorithm to estimate the direction-of-arrivals of multiple acoustic sources is studied. A new energy-based acoustic source localization scheme is proposed and the associated Cramer– Rao lower bound analysis is presented. A novel acoustic target tracking system is presented using the random set theory. A new system using the nonlinear extended Kalman filter and Hsiao-Chun Wu (S’97-M’99-SM’05) received the Ph.D. degree in electrical and computer engineering from the University of Florida, Gainesville, FL, USA, in 1999. From March 1999 to January 2001, he was with the Motorola Personal Communications Sector Research Labs as a Senior Electrical Engineer. In 2001, he joined the faculty in the Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, USA. From August 2008 to December 2008, he was a Visiting Associate Professor at the Department of Electrical Engineering, Stanford University, Stanford, CA, USA. He has published more than 170 peer-refereed technical journal and conference articles in electrical and computer engineering. the particle filter (PF) along with interacting multiple model algorithm is proposed for maneuvering mobile station tracking with bearings-only measurements. These ten papers cover a broad scope of experimental measurements, algorithm design, system integration, and theoretical analysis. They may help the scientists to better understand and utilize sensor networks in advanced localization and target-tracking applications for both civil and military purposes.

Acoustic DOA Estimation: An Approximate Maximum Likelihood Approach

In this paper, we study the approximate maximum likelihood (AML) algorithm that estimates the direction of arrivals (DOA) of multiple acoustic sources. The AML algorithm yields the DOAs of multiple sources by enabling an alternating projection procedure based on sequentially iterative search over the single-source parameter space. Through our evaluations on blind beamforming, we have been able to not only use a sensor array with the 2-D AML algorithm that estimates the azimuth angles for multiple sources in the far field of the array but also employ the 3-D AML algorithm that estimates the azimuth and elevation angles. To present a few justifying examples, we consider two acoustic sources and present Cramér-Rao bound (CRB) on the DOA estimation given by a 3-D array. Also, we adopt the concept of isotropic array that yields constant CRB on azimuth angles over the whole azimuth range and constant CRB on elevation angles over the whole elevation range. We have also shown by simulations and experimentally collected data that the 3-D AML algorithm exhibits superior source separation capability over its 2-D counterpart for multiple sources located at different azimuth and elevation angles.

Incident Signal Power Comparison for Localization of Concurrent Multiple Acoustic Sources

In this paper, a method to solve the localization of concurrent multiple acoustic sources in large open spaces is presented. The problem of the multisource localization in far-field conditions is to correctly associate the direction of arrival (DOA) estimated by a network array system to the same source. The use of systems implementing a Bayesian filter is a traditional approach to address the problem of localization in multisource acoustic scenario. However, in a real noisy open space the acoustic sources are often discontinuous with numerous short-duration events and thus the filtering methods may have difficulty to track the multiple sources. Incident signal power comparison (ISPC) is proposed to compute DOAs association. ISPC is based on identifying the incident signal power (ISP) of the sources on a microphone array using beamforming methods and comparing the ISP between different arrays using spectral distance (SD) measurement techniques. This method solves the ambiguities, due to the presence of simultaneous sources, by identifying sounds through a minimization of an error criterion on SD measures of DOA combinations. The experimental results were conducted in an outdoor real noisy environment and the ISPC performance is reported using different beamforming techniques and SD functions.

Source localisation using sparse acoustic vector sensor array

This paper addresses the problem of passive source localisation using sparse autonomous underwater vehicle towed array. Any reduction in the sensory signal processing hardware complexity without affecting the acoustic performance significantly improves the endurance capability of the unmanned vehicle. In this paper, we propose a novel sparse acoustic vector sensor (SAVS) array architecture to estimate the direction of arrival (DoA) of multiple acoustic sources. Bearing localisation is effectively achieved by customising the sparse reconstruction pursuit algorithms to suit the SAVS array. The localisation performance of the proposed architecture is analysed and compared with the conventional acoustic pressure sensor (APS) and acoustic vector sensor (AVS)-based array architectures. Theoretical formulation and Monte Carlo simulations for sparse towed array operation in deep ocean is presented. We also develop the expression for the Cramer-Rao bound (CRB) of the variance of DoA for localisation of multiple acoustic sources in generalised Gaussian noise for any AVS array configuration. This proposed architecture enhances the endurance of the vehicle by significantly reducing the number of acoustic sensors, signal conditioning hardware, transmission data rate, number of snapshots and software complexity.

Passive Source Localization Using Compressively Sensed Towed Array

The objective of this work is to estimate the sparse angular power spectrum using a towed acoustic pressure sensor (APS) array. In a passive towed array sonar, any reduction in the analog sensor signal conditioning receiver hardware housed inside the array tube, significantly improves the signal integrity and hence the localization performance. In this paper, a novel sparse acoustic pressure sensor (SAPS) array architecture is proposed to estimate the direction of arrival (DOA) of multiple acoustic sources. Bearing localization is effectively achieved by customizing the Capons spatial filter algorithm to suit the SAPS array architecture. Apart from the Monte Carlo simulations, the acoustic performance of the SAPS array with compressively sensed minimum variance distortionless response (CS-MVDR) filter is demonstrated using a real passive towed array data. The proposed sparse towed array architecture promises a significant reduction in the analog signal acquisition receiver hardware, transmission data rate, number of snapshots and software complexity. Defence Science Journal, 2013, 63(6), pp.630 -635 , DOI:http://dx.doi.org/10.14429/dsj.63.5765

A Real-Time Multiple Acoustic Sources Localization Method Using Widely Spaced Microphone Array

A real-time disambiguous localization method for multiple acoustic sources using widely spaced microphone array is proposed in this paper. The observation signals in each frame are firstly discriminated utilizing conventional energy-based sound detection measurement. Using inter-microphone phase differences (IPD), as well as an iterative cluster process, the DOAs of sources can be estimated while the phase wrapping ambiguity in conventional DUET is avoided.

Doppler Distortion Removal Method for Multiple Acoustic Sources

In wayside fault diagnosis of train bearings, the phenomenon of Doppler distortion in the acoustic signal of moving acoustic source acquired with a microphone leads to the difficulty for signal analysis. In this paper, a new method based on Dopplerlet transform and re-sampling is proposed to eliminate the Doppler distortion of multiple acoustic sources which provide a reference for wayside fault diagnosis of train bearings. Firstly, search the parameters space to find the primary functionsDopplerlet atoms. According to the Morse acoustic theory, the instantaneous frequency of the Dopplerlet atom which we choose to remove Doppler distortion of the corresponding acoustic source can be acquired. Then, the re-sampling sequence can be established in time domain. Through the resample, the Doppler distortion can be removed. In the end of this paper, an experiment with practical acoustic signals is carried out, and the results verified the effectiveness of this method.

Multiple wideband source detection and tracking using a distributed acoustic vector sensor array: A random finite set approach

In the past, distributed acoustic vector sensor (AVS) arrays have been employed to localize the source in a three dimensional space. Least-squares approaches were introduced to triangulate the source position by using the direction of arrival (DOA) measurements extracted at each AVS. However, such approaches: (1) cannot detect and localize multiple sources; and (2) can be seriously degraded due to inaccurate DOA estimates. In this paper, a practical scenario that the source existence and the number of sources are assumed to be unknown is considered. A random finite set (RFS) approach is developed to jointly detect and track multiple wideband acoustic sources. RFS is able to characterize the randomness of the state process (i.e., the source dynamics and the number of active sources) as well as the measurement process (i.e., DOA measurements generated by real sources and false alarms). Since the relationship between DOAs and source position is highly nonlinear, a particle filtering approach is employed to arrive at a computationally tractable approximation of the RFS densities. Simulations under different acoustic environments demonstrate the performance of the proposed approach and show a significant improvement on position estimation over the least squares approaches.

Signal processing for a positioning system with binary sensory outputs

Energy based localization method for source localization has attracted considerable attention in recent years. The binary sensor is a low-power and bandwidth-efficient solution for positioning system since the node provides only binary information about the sources. Most of the existing acoustic source localization methods assume there only exists a single source and may fail with the presence of multiple sources. In contrast, we directly consider the case when the node is influenced by multiple sources. A maximum likelihood estimation method is applicable to the multiple sources localization problems, but is at the cost of high computational complexity. To tackle these problems, this paper proposes a novel likelihood matrix based multiple acoustic sources localization algorithm for binary sensor. The fuzzy c-means algorithm is firstly employed to calculate the membership degrees of the alarmed nodes associated with the sources, and then a likelihood matrix is proposed for multiple acoustic sources localization. Simulation and experiment results show that the proposed algorithm provides accurate location estimations.

The role of diffusive architectural surfaces on auditory spatial discrimination in performance venues

In musical or theatrical performance, some venues allow listeners to individually localize and segregate individual performers, while others produce a well blended ensemble sound. The room acoustic conditions that make this possible, and the psycho-acoustic effects at work are not fully understood. This research utilizes auralizations from measured and simulated performance venues to investigate spatial discrimination of multiple acoustic sources in rooms. Signals were generated from measurements taken in a small theater, and listeners in the audience area were asked to distinguish pairs of speech sources on stage with various spatial separations. This experiment was repeated with the proscenium splay walls treated to be flat, diffusive, or absorptive. Similar experiments were conducted in a simulated hall, utilizing 11 early reflections with various characteristics, and measured late reverberation. The experiments reveal that discriminating the lateral arrangement of two sources is possible at narrower separation angles when reflections come from flat or absorptive rather than diffusive surfaces.

Localization of multiple acoustic sources in a room environment

Combustion systems results in high pollution and low performance due to lack of uniform thermal and chemical behavior in the entire reactor zone. Localization of multiple acoustic sources using an array of microphones and time difference of arrival (TDOA) method is examined. Adaptations of such a scheme into high speed jet flows, combustors or reactors can help assist in the identification of local acoustic sources in a system to seek improved performance of the system. Linear microphones can be used for acoustic source localization in a practical room environment using time delay estimation. Hyperbolic position algorithm based on TDOA provides good localization of a single acoustic in both two dimensional and three dimensional space. The time delay between the signals acquired by two microphones is obtained from maximum of cross-correlation between the two signals. Noise and reverberation effects can introduce ambiguity in the time delay estimation from multiple peaks in the delay estimator. In addition directional incidence from multiple sources can result in cross talk between different sources to degrade localization accuracy. CPSP (Cross-power spectral phase analysis) is examined here for localization of multiple sound sources present in a room environment. The CPSP method localizes a sound source as a crossing point of sound directions estimated using different microphone pairs but it degrades due to cross-correlation between different sound sources. A new method based on averaging of CPSP coefficients has been examined here to reduce the directional ambiguity of the wave front direction angle of multiple sound sources. Experiment results in a real room environment validated the accuracy and precision of the above method for the estimation of directions of multiple sound sources. Experiment results also showed that noise and reverberation effects can be incorporated by averaging CPSP data over larger pairs of microphones. Subsequently localization of multiple sources was accomplished in a room environment by using hyperbolic position estimation. These results provide a foundation to develop methodologies for smart, low noise, low pollution and highly efficient combustors and gasifiers through seeking uniform reaction field in the entire reaction zone.

Bayesian tracking of multiple acoustic sources in an uncertain ocean environment

This letter develops a Bayesian approach to matched-field tracking of multiple acoustic sources in a poorly-known environment. Markov-chain Monte Carlo methods explicitly sample the posterior probability density over source locations and environmental parameters, while analytic maximum-likelihood solutions for complex source strengths and noise variance in terms of the explicit parameters allow these parameters to be sampled efficiently. This produces a time-ordered sequence of joint marginal probability distributions over source range and depth, from which optimal track estimates and uncertainties are extracted. Synthetic examples consider tracking a submerged source in the presence of a louder shallow interferer in an unknown environment.

Particle Filtering Approaches for Multiple Acoustic Source Detection and 2-D Direction of Arrival Estimation Using a Single Acoustic Vector Sensor

This paper considers the problem of tracking multiple acoustic sources using a single acoustic vector sensor (AVS). Firstly, a particle filtering (PF) approach is developed to track the direction of arrivals of fixed and known number of sources. Secondly, a more realistic tracking scenario which assumes that the number of acoustic sources is unknown and time-varying is considered. A random finite set (RFS) framework is employed to characterize the randomness of the state process, i.e., the dynamics of source motion and the number of active sources, as well as the measurement process. As deriving a closed-form solution for the multi-source probability density is difficult, a particle filtering approach is employed to arrive at a computationally tractable approximation of the RFS densities. The proposed RFS-PF algorithm is able to simultaneously detect and track multiple sources. Simulations under different tracking scenarios demonstrate the ability of the proposed approaches in tracking multiple acoustic sources.