Source localization for multiple speech sources using low complexity non-parametric source separation and clustering

Abstract

This article presents a new method for localization of multiple concurrent speech sources that relies on simultaneous blind signal separation and direction of arrival (DOA) estimation, as well as a method to solve the intersection point selection problem that arises when locating multiple speech sources using multiple sensor arrays. The proposed method is based on a low complexity nonparametric blind signal separation method, making is suitable for real-time applications on embedded platforms. On top of reduced complexity in comparison to a previously presented method, the DOA estimation accuracy is also improved. Evaluation of the performance is done with both real recording and simulations, and a real-time prototype of the proposed method has been implemented on a DSP platform to evaluate the computational and the memory complexities in a real application.

A Survey of Sound Source Localization Methods in Wireless Acoustic Sensor Networks

“…In [78] such features are extracted using Blind Source Separation. The features are binary masks [79] in the frequency domain for each detected source that when applied to the corresponding source signals they perform source separation.…”

Real-Time Multiple Sound Source Localization and Counting Using a Circular Microphone Array

“…The summation in (20) takes place over all frequency components and ratios in all the columns of the mixing matrices. For non-linear function g(E(T)), we use the kernel-based one recommended by the authors of [18] g(E(T)) = 1 ω e…”

“…Using this assumption, the multiple source propagation estimation problem may be rewritten as a single-source one in these windows or zones, and the above methods estimate a mixing/propagation matrix, and then try to recover the sources. By estimating this mixing matrix and knowing the geometry of the microphone array, we may localize the sources, as proposed in [20]- [22], for example. Most of the SCA approaches require the sources to be W-disjoint orthogonal (WDO) [23]-meaning that in each time-frequency component, at most one source is activewhich is approximately satisfied by speech in anechoic environments, but not in reverberant conditions.…”

“…Unfortunately, most of the SCA methods and their DOA extensions are computationally intensive and therefore off-line methods (e.g., [21] and the references within). The work of [20] is a frame-based method, but requires WDO sources.…”

Acoustic source localization in mixed field using spherical microphone arrays

“…If the source is in 3D space, the number of basis is large and the computational complexity is high. Some approaches assumed that one source was dominant over the others in some time-frequency zones [11,33]. They extended the single-source DOA algorithm over these zones to estimate multiple source locations.…”