Localization of sound sources : a systematic review

Sound localization is a vast field of research and advancement which is used in many useful applications to facilitate communication, radars, medical aid, and speech enhancement to but name a few. Many different methods are presented in recent times in this field to gain benefits. Various types of microphone arrays serve the purpose of sensing the incoming sound. This paper presents an overview of the importance of using sound localization in different applications along with the use and limitations of ad-hoc microphones over other microphones. In order to overcome these limitations certain approaches are also presented. Detailed explanation of some of the existing methods that are used for sound localization using microphone arrays in the recent literature is given. Existing methods are studied in a comparative fashion along with the factors that influence the choice of one method over the others. This review is done in order to form a basis for choosing the best fit method for our use

Over-Determined Source Separation and Localization Using Distributed Microphones

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1

DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays

Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In this paper, we propose to extend a previously introduced distributed DNN-based time-frequency mask estimation scheme that can efficiently use spatial information in form of so-called compressed signals which are pre-filtered target estimations. We study the performance of this algorithm under realistic acoustic conditions and investigate practical aspects of its optimal application. We show that the nodes in the microphone array cooperate by taking profit of their spatial coverage in the room. We also propose to use the compressed signals not only to convey the target estimation but also the noise estimation in order to exploit the acoustic diversity recorded throughout the microphone array.Comment: Submitted to TASL

Structured Sparsity Models for Reverberant Speech Separation

We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery. We propose a scheme for characterizing the room acoustic from the unknown competing speech sources relying on localization of the early images of the speakers by sparse approximation of the spatial spectra of the virtual sources in a free-space model. The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source. This enables us to identify the early support of the room impulse response function and its unique map to the room geometry. To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting joint sparsity model formulated upon spatio-spectral sparsity of concurrent speech representation. The acoustic parameters are then incorporated for separating individual speech signals through either structured sparse recovery or inverse filtering the acoustic channels. The experiments conducted on real data recordings demonstrate the effectiveness of the proposed approach for multi-party speech recovery and recognition

Self-localization in Ad Hoc Indoor Acoustic Networks

The increasing use of mobile technology in everyday life has aroused interest into developing new ways of utilizing the data collected by devices such as mobile phones and wearable devices. Acoustic sensors can be used to localize sound sources if the positions of spatially separate sensors are known or can be determined. However, the process of determining the 3D coordinates by manual measurements is tedious especially with increasing number of sensors. Therefore, the localization process has to be automated. Satellite based positioning is imprecise for many applications and requires line-of-sight to the sky. This thesis studies localization methods for wireless acoustic sensor networks and the process is called self-localization.This thesis focuses on self-localization from sound, and therefore the term acoustic is used. Furthermore, the development of the methods aims at utilizing ad hoc sensor networks, which means that the sensors are not necessarily installed in the premises like meeting rooms and other purpose-built spaces, which often have dedicated audio hardware for spatial audio applications. Instead of relying on such spaces and equipment, mobile devices are used, which are combined to form sensor networks.For instance, a few mobile phones laid on a table can be used to create a sensor network built for an event and it is inherently dismantled once the event is over, which explains the use of the term ad hoc. Once positions of the devices are estimated, the network can be used for spatial applications such as sound source localization and audio enhancement via spatial filtering. The main purpose of this thesis is to present the methods for self-localization of such an ad hoc acoustic sensor network. Using off-the-shelf ad hoc devices to establish sensor networks enables implementation of many spatial algorithms basically in any environment.Several acoustic self-localization methods have been introduced over the years. However, they often rely on specialized hardware and calibration signals. This thesis presents methods that are passive and utilize environmental sounds such as speech from which, by using time delay estimation, the spatial information of the sensor network can be determined. Many previous self-localization methods assume that audio captured by the sensors is synchronized. This assumption cannot be made in an ad hoc sensor network, since the different sensors are unaware of each other without specific signaling that is not available without special arrangement.The methods developed in this thesis are evaluated with simulations and real data recordings. Scenarios in which the targets of positioning are stationary and in motion are studied. The real world recordings are made in closed spaces such as meeting rooms. The targets are approximately 1 – 5 meters apart. The positioning accuracy is approximately five centimeters in a stationary scenario, and ten centimeters in a moving-target scenario on average. The most important result of this thesis is presenting the first self-localization method that uses environmental sounds and off-the-shelf unsynchronized devices, and allows the targets of self-localization to move

Robust Distributed Multi-Source Detection and Labeling in Wireless Acoustic Sensor Networks

The growing demand in complex signal processing methods associated with low-energy large scale wireless acoustic sensor networks (WASNs) urges the shift to a new information and communication technologies (ICT) paradigm. The emerging research perception aspires for an appealing wireless network communication where multiple heterogeneous devices with different interests can cooperate in various signal processing tasks (MDMT). Contributions in this doctoral thesis focus on distributed multi-source detection and labeling applied to audio enhancement scenarios pursuing an MDMT fashioned node-specific source-of-interest signal enhancement in WASNs. In fact, an accurate detection and labeling is a pre-requisite to pursue the MDMT paradigm where nodes in the WASN communicate effectively their sources-of-interest and, therefore, multiple signal processing tasks can be enhanced via cooperation. First, a novel framework based on a dominant source model in distributed WASNs for resolving the activity detection of multiple speech sources in a reverberant and noisy environment is introduced. A preliminary rank-one multiplicative non-negative independent component analysis (M-NICA) for unique dominant energy source extraction given associated node clusters is presented. Partitional algorithms that minimize the within-cluster mean absolute deviation (MAD) and weighted MAD objectives are proposed to determine the cluster membership of the unmixed energies, and thus establish a source specific voice activity recognition. In a second study, improving the energy signal separation to alleviate the multiple source activity discrimination task is targeted. Sparsity inducing penalties are enforced on iterative rank-one singular value decomposition layers to extract sparse right rotations. Then, sparse non-negative blind energy separation is realized using multiplicative updates. Hence, the multiple source detection problem is converted into a sparse non-negative source energy decorrelation. Sparsity tunes the supposedly non-active energy signatures to exactly zero-valued energies so that it is easier to identify active energies and an activity detector can be constructed in a straightforward manner. In a centralized scenario, the activity decision is controlled by a fusion center that delivers the binary source activity detection for every participating energy source. This strategy gives precise detection results for small source numbers. With a growing number of interfering sources, the distributed detection approach is more promising. Conjointly, a robust distributed energy separation algorithm for multiple competing sources is proposed. A robust and regularized $t_{\nu}M$-estimation of the covariance matrix of the mixed energies is employed. This approach yields a simple activity decision using only the robustly unmixed energy signatures of the sources in the WASN. The performance of the robust activity detector is validated with a distributed adaptive node-specific signal estimation method for speech enhancement. The latter enhances the quality and intelligibility of the signal while exploiting the accurately estimated multi-source voice decision patterns. In contrast to the original M-NICA for source separation, the extracted binary activity patterns with the robust energy separation significantly improve the node-specific signal estimation. Due to the increased computational complexity caused by the additional step of energy signal separation, a new approach to solving the detection question of multi-device multi-source networks is presented. Stability selection for iterative extraction of robust right singular vectors is considered. The sub-sampling selection technique provides transparency in properly choosing the regularization variable in the Lasso optimization problem. In this way, the strongest sparse right singular vectors using a robust $\ell_1$-norm and stability selection are the set of basis vectors that describe the input data efficiently. Active/non-active source classification is achieved based on a robust Mahalanobis classifier. For this, a robust $M$-estimator of the covariance matrix in the Mahalanobis distance is utilized. Extensive evaluation in centralized and distributed settings is performed to assess the effectiveness of the proposed approach. Thus, overcoming the computationally demanding source separation scheme is possible via exploiting robust stability selection for sparse multi-energy feature extraction. With respect to the labeling problem of various sources in a WASN, a robust approach is introduced that exploits the direction-of-arrival of the impinging source signals. A short-time Fourier transform-based subspace method estimates the angles of locally stationary wide band signals using a uniform linear array. The median of angles estimated at every frequency bin is utilized to obtain the overall angle for each participating source. The features, in this case, exploit the similarity across devices in the particular frequency bins that produce reliable direction-of-arrival estimates for each source. Reliability is defined with respect to the median across frequencies. All source-specific frequency bands that contribute to correct estimated angles are selected. A feature vector is formed for every source at each device by storing the frequency bin indices that lie within the upper and lower interval of the median absolute deviation scale of the estimated angle. Labeling is accomplished by a distributed clustering of the extracted angle-based feature vectors using consensus averaging