Localization Of Multiple Acoustic Sources Research Articles

Disambiguation of measurements for multiple acoustic source localization using deep multi-dimensional assignments

The location estimation of multiple simultaneously active sources in an acoustic sensor network is quite challenging because the correct combination of measurements received from different microphone arrays is not usually known, generally termed the data association problem. Existing techniques generally formulate the data association problem as a multidimensional assignment and solve them using classical optimal techniques. Although these approaches show effectiveness for a limited amount of active sources, the computational time and complexity of these techniques increase significantly when the microphones or acoustic sources increase. In this paper, a learning approach is proposed that solves the multidimensional assignment using a deep neural network. Initially, the features that are employed for the association are formulated for each of the detected sources in all the array nodes, and a multidimensional assignment problem is formulated. Subsequently, a deep multidimensional assignment network is devised to extract the correspondence probability of the measurements received from the microphone arrays. Specifically, a data-driven differentiable approach is presented for multidimensional assignments that is computationally efficient. The proposed methodology is validated under realistic conditions for both speech and urban signals. The methodology is compared to state-of-the-art methods for showing the performance gain in terms of accuracy of association and location estimation with a reduced computational time.

Multiple acoustic source localization using deep data association

Acoustic source localization is a key component of the various acoustic monitoring systems. In this paper, we address the data association problem occurring in the localization of multiple acoustic sources for a distributed acoustic sensor network. The direction of arrival (DOA) of multiple signals is computed at each node and forwarded to a central system for localization. A challenging problem known as data association occurs because DOAs received from different nodes belonging to the same acoustic source are not known. In a realistic environment, the complexity of the data association method increases with the increase in the number of array nodes and sources. With the increase in complexity, the time required for estimating the location of all the sources also increases. Time-critical scenarios of acoustic monitoring require the location to be estimated as close to real-time as possible. To overcome this problem a data-driven deep learning-based solution is presented to control the complexity of the system. A learning approach that formulates the data association among DOAs as combinatorial optimization is proposed. Firstly, features are extracted along with the DOAs of every detected source at each node in the network. Subsequently, a dynamic multiplex Long Short-term memory(LSTM) network is formulated to estimate the association probabilities of the DOAs from different nodes. The methodology is tested using simulations and real data experiments. Comparative analysis is done to validate that the methodology achieves higher association and localization accuracy with a reduced computational time as compared to present state-of-the-art methods.

Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks

A method for multiple acoustic source localization using a tetrahedral microphone array and a convolutional neural network (CNN) is presented. Our method presents a novel approach for the estimation of acoustic source direction of arrival (DoA), both azimuth and elevation, utilizing a non-coplanar microphone array. In our approach, we use the phase component of the short-time Fourier transform (STFT) of the microphone array’s signals as the input feature for the CNN and a DoA probability density map as the training target. Our findings imply that our method outperforms the currently available methods for multiple sound source DoA estimation in both accuracy and speed.

Iterative Diagonal Unloading Beamforming for Multiple Acoustic Sources Localization Using Compact Sensor Arrays

Compact acoustic sensor arrays are nowadays popular devices for audio sensing due to minimum size and lightness characteristics that make it attractive for numerous applications such as human-computer interaction, acoustic scene analysis, teleconferencing systems, hearing aid, robotics, and bioacoustics. A compact array has a small inter-microphone distance that allows to reduce the spatial aliasing effect in the acoustic source localization problem, degrading however the spatial resolution. Thus, as a consequence, the capability of this class of sensors in the localization of multiple acoustic sources is rather limited. In this paper, we present an iterative diagonal unloading (IDU) beamforming suitable for the direction of arrivals (DOAs) estimation of concurrent multiple sources using compact microphone arrays. The method is based on the identification of the dominant signal, which corresponds to the largest peak in the pseudo-spectrum (i.e., the acoustic response map) computed by the DU beamformer, and on an iterative cancellation of the dominant signal from the covariance matrix to compute a new spatial pseudo-spectrum. All the pseudo-spectrum responses computed with the iterative procedure are summed to obtain a high resolution map of the acoustic scene. We analyze the DOAs estimation performance using compact uniform linear arrays (ULAs) and spherical microphone arrays (SMAs) with simulations and real acoustic data. The results show that the proposed IDU beamforming increases the spatial resolution allowing an effective localization of concurrent multiple sources.

A feature-based data association method for multiple acoustic source localization in a distributed microphone array

Multisource localization using time difference of arrival (TDOA) is challenging because the correct combination of TDOA estimates across different microphone pairs, corresponding to the same source, is usually unknown, which is termed as the data association problem. Moreover, many existing multisource localization techniques are originally demonstrated in two dimensions, and their extensions to three dimensions (3D) are not straightforward and would lead to much higher computational complexity. In this paper, we propose an efficient, feature-based approach to tackle the data association problem and achieve multisource localization in 3D in a distributed microphone array. The features are generated by using interchannel phase difference (IPD) information, which indicates the number of times each frequency bin across all time frames has been assigned to sources. Based on such features, the data association problem is addressed by correlating most similar features across different microphone pairs, which is executed by solving a two-dimensional assignment problem successively. Thereafter, the locations of multiple sources can be obtained by imposing a single-source location estimator on the resulting TDOA combinations. The proposed approach is evaluated using both simulated data and real-world recordings.

Multiple Acoustic Source Localization in Microphone Array Networks

The problem of multiple acoustic source localization using observations from a microphone array network is investigated in this article. Multiple source signals are assumed to be window-disjoint-orthogonal (WDO) on the time-frequency (TF) domain and time delay of arrival (TDOA) measurements are extracted at each TF bin. A Bayesian network model is then proposed to jointly assign the measurements to different sources and estimate the acoustic source locations. Considering that the WDO assumption is usually violated under reverberant and noisy environments, we construct a relational network by coding the distance information between the distributed microphone arrays such that adjacent arrays have higher probabilities of observing the same acoustic source, which is able to mitigate the miss detection issues in adverse environments. A Laplace approximate variational inference method is introduced to estimate the hidden variables in the proposed Bayesian network model. Both simulations and real data experiments are performed. The results show that our proposed method is able to achieve better source localization accuracy than existing methods.

TDOA-Based Multiple Acoustic Source Localization Without Association Ambiguity

Multiple source localization using time-differences of arrival (TDOAs) is challenging because of the ambiguity involved in associating the TDOAs computed across microphone pairs to the sources. We show that the association ambiguity of the TDOAs can be effectively resolved using the concept of an inverse delay interval region (IDIR), which we introduce in this paper. By examining the association between a spatial domain and the TDOAs, we define IDIR as an interhyperboloidal spatial region corresponding to an interval of delays for a given pair of microphones. The proposed scheme for localizing multiple sources involves two stages. In the first stage, the given enclosure is partitioned into nonoverlapping elemental regions and the ones that contain a source are detected using a measure based on the generalized cross-correlation with phase transform and the IDIRs. In the second stage, the sources are finely localized within each of the detected elemental regions by identifying the IDIRs containing a single source and a novel region-constrained localization approach. We evaluate the performance of the proposed approach on real recordings from the AV16.3 corpus and in a simulated reverberation setting with a reverberation time RT60 of up to 500 ms, and show that the DOA estimation error with two active speakers is within 2° and the spatial localization error is less than 30 cm for each speaker.

Acoustic Sources Localization in 3D Using Multiple Spherical Arrays

Direction of arrival (DOA) estimation of multiple sources using sensor arrays has been widely studied in the last few decades, particularly, the spherical harmonic analysis utilizing a spherical array. Both the number of sensors on the aperture and size of the sphere can affect the estimation accuracy dramatically. However, those two factors are conflicted to each other in a single spherical array. In this paper, a multiple spherical arrays structure is proposed to provide an alternative design to the traditional single spherical array for the spherical harmonic decomposition, to obtain better localization performance. The new structure consists of several identical spheres in a given area, and the microphones are placed identically on each sphere. The spherical harmonic analysis algorithm using the new multiple array structure for the problem of multiple acoustic sources localization is presented. Simulation results show that the multiple spherical arrays can provide a more accurate direction of arrival (DOA) estimation for the multiple sources than that of a single spherical array, distinguish several adjacent sources more efficiently, and reduce the number of microphones on each sphere without decreasing its’ estimation accuracy.

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.

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.

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.

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 multiple-source localization in an uncertain ocean environment

This paper considers simultaneous localization of multiple acoustic sources when properties of the ocean environment (water column and seabed) are poorly known. A Bayesian formulation is developed in which the environmental parameters, noise statistics, and locations and complex strengths (amplitudes and phases) of multiple sources are considered to be unknown random variables constrained by acoustic data and prior information. Two approaches are considered for estimating source parameters. Focalization maximizes the posterior probability density (PPD) over all parameters using adaptive hybrid optimization. Marginalization integrates the PPD using efficient Markov-chain Monte Carlo methods to produce joint marginal probability distributions for source ranges and depths, from which source locations are obtained. This approach also provides quantitative uncertainty analysis for all parameters, which can aid in understanding of the inverse problem and may be of practical interest (e.g., source-strength probability distributions). In both approaches, closed-form maximum-likelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. Examples are presented of both approaches applied to single- and multi-frequency localization of multiple sources in an uncertain shallow-water environment, and a Monte Carlo performance evaluation study is carried out.

Simultaneous Bayesian localization of multiple sources in an uncertain ocean environment.

This paper considers simultaneous localization of multiple acoustic sources when properties of the ocean environment (water column and seabed) are poorly known. A Bayesian formulation is developed in which the environmental parameters, noise variance, and locations and complex strengths (amplitudes and phases) of multiple sources are considered unknown random variables constrained by acoustic data and prior information. Two approaches are considered for estimating source locations and strengths. The first approach, referred to as focalization, maximizes the posterior probability density (PPD) over all parameters using an adaptive hybrid optimization. The second approach, referred to as marginalization, integrates the PPD to produce marginal probability distributions for source positions and strengths, which quantify localization uncertainties. In this approach, 2‐D Gibbs sampling is applied to source ranges and depths, and Metropolis–Hastings sampling is applied in principal‐component space for environmental parameters. In both approaches, closed‐form maximum‐likelihood expressions for source strengths and noise variance allow these parameters to be sampled implicitly rather than explicitly, reducing the dimensionality of the inversion. Examples are presented of both approaches applied to single‐ and multi‐frequency localization of multiple sources in an uncertain shallow‐water environment.

High-Resolution Multiple Wideband and Nonstationary Source Localization With Unknown Number of Sources

In this paper, a new algorithm for high-resolution multiple wideband and nonstationary source localization using a sensor array is proposed. The received signals of the sensor array are first converted into the time-frequency domain via short-time Fourier transform (STFT) and we find that a set of short-time power spectrum matrices at different time instants have the joint diagonalization structure in each frequency bin. Based on such joint diagonalization structure, a novel cost function is designed and a new spatial spectrum for direction-of-arrival (DOA) estimation at hand is derived. Compared to the maximum-likelihood (ML) method with high computational complexity, the proposed algorithm obtains the DOA estimates via one-dimensional (1-D) search instead of multidimensional search. Therefore its computational complexity is much lower than the ML method. Unlike the subspace-based high-resolution DOA estimation techniques, it is not necessary to determine the number of sources in advance for the proposed algorithm. Moreover, the proposed method is robust to the effects of reverberation caused by multipath reflections. Hence it is suitable for multiple acoustic source localization in a reverberant room. The results of numerical simulations and experiments in a real room with a moderate reverberation are provided to demonstrate the good performance of the proposed approach.

Low complexity multiple acoustic source localization in sensor networks based on energy measurements

This work addresses the problem of estimating the locations of multiple acoustic sources by a network of distributed energy measuring sensors. The maximum likelihood (ML) solution to this problem is related to the optimization of a non-convex function of, usually, many variables. Thus, search-based methods of high complexity are required in order to yield an accurate solution. Considerable reduction of the complexity can be achieved by means of an alternating projection (AP) algorithm that decomposes the original problem into a number of simpler, yet also non-convex, optimization steps. The particular form of the derived cost functions of each such optimization step indicates that, in some cases, an approximate form of these cost functions can be used. These approximate cost functions can be evaluated using considerably lower computational complexity. Thus, a low-complexity version of the AP algorithm is proposed. Extensive simulation results demonstrate that the proposed algorithm offers a performance close to that of the exact AP implementation, and in some cases, similar performance to that of the ML estimator.

Localization of multiple acoustic sources with small arrays using a coherence test

Direction finding of more sources than sensors is appealing in situations with small sensor arrays. Potential applications include surveillance, teleconferencing, and auditory scene analysis for hearing aids. A new technique for time-frequency-sparse sources, such as speech and vehicle sounds, uses a coherence test to identify low-rank time-frequency bins. These low-rank bins are processed in one of two ways: (1) narrowband spatial spectrum estimation at each bin followed by summation of directional spectra across time and frequency or (2) clustering low-rank covariance matrices, averaging covariance matrices within clusters, and narrowband spatial spectrum estimation of each cluster. Experimental results with omnidirectional microphones and colocated directional microphones demonstrate the algorithm's ability to localize 3-5 simultaneous speech sources over 4 s with 2-3 microphones to less than 1 degree of error, and the ability to localize simultaneously two moving military vehicles and small arms gunfire.

Localization of multiple acoustic sources in the shallow ocean

An iterative, rotated coordinates inversion technique is applied in conjunction with spatial narrowband filters in matrix form to localize multiple acoustic sources in the shallow ocean. When the iterative, rotated coordinates inversion technique is applied to broadband horizontal line array data designed to simulate a group of moving ships, the bearing of the loudest source is quickly identified. Both passband and stop band matrix filters are constructed for an angular sector centered on the identified bearing. Data filtered with the passband filters are used in the inversion to obtain estimates of the remaining source parameters, range, depth, course angle and speed, and prominent environmental parameters. Additional inversions applied to data modified by the stop band filters yield the bearing of the next loudest source. The procedure is repeated until all detectable sources have been localized. Results of applying this strategy to several simulated cases are presented.

Multiple acoustic source localization using ambiguous phase differences under reverberative conditions

“Phase ambiguity” leads to confusion in computational source localization where multiple source locations are introduced from a cross-spectral phase value measured by two sensors at high frequencies, where sound wavelength is shorter than sensor interval. In this paper, a frequency domain algorithm for broadband source localization by two sensors is proposed for solving “phase ambiguity” confusion under actual conditions. Using the overlapped and averaged phase differences of the cross-spectral phases measured over the audible frequency range, multiple source azimuths are identified from each phase difference as much as possible over the azimuth range of ±90°. The frequency-independent azimuths extracted from multiple azimuths by Hough transformation provide the target source azimuths. The azimuths for the two loudspeakers can be identified simultaneously in this way from the phase differences measured over the full audible frequency range within an error of approximately 6° under reverberative conditions. By removing the numerical noise during source azimuth identification, the estimated source distribution corresponds to the diameters of the loudspeakers. When it is necessary to distinguish between near or far sound sources around the microphones, the horizontal azimuths for the sources can be precisely identified from all directions except at approximately ±90° if judgment of the front or back is given.