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

Abstract

Abstract-In this work, a multiple sound source localization and counting method is presented, that imposes relaxed sparsity constraints on the source signals. A uniform circular microphone array is used to overcome the ambiguities of linear arrays, however the underlying concepts (sparse component analysis and matching pursuit-based operation on the histogram of estimates) are applicable to any microphone array topology. Our method is based on detecting time-frequency (TF) zones where one source is dominant over the others. Using appropriately selected TF components in these "single-source" zones, the proposed method jointly estimates the number of active sources and their corresponding directions of arrival (DOAs) by applying a matching pursuit-based approach to the histogram of DOA estimates. The method is shown to have excellent performance for DOA estimation and source counting, and to be highly suitable for real-time applications due to its low complexity. Through simulations (in various signal-to-noise ratio conditions and reverberant environments) and real environment experiments, we indicate that our method outperforms other state-of-the-art DOA and source counting methods in terms of accuracy, while being significantly more efficient in terms of computational complexity.

Detecting multiple, simultaneous talkers through localising speech recorded by ad-hoc microphone arrays

“…The features explained in section II can be applied within a surface fitting method for source localisation [8] and if more than one active source (peaks of the surface) is detected and localised, multiple, simultaneous sources are assumed to be active. Source counting can be performed based on their Direction of Arrivals (DOAs) [17] however sources with identical DOAs cannot be discriminated by the proposed method of [17] and in some applications DOA estimation leads to detection of one virtual source instead of two sources at different angles [18]. In order to discriminate and count sources with identical DOAs herein active sources and their 2D locations (x and y coordinates) are determined.…”

“…Most source localisation and speech separation algorithms assume that sources are W-disjoint orthogonal [17] which means at each time-frequency component at most one source is active. The multiple source localisation algorithm proposed in this paper relates the extracted features (Section II) to spatial distances between active sources and all the nodes in a room with known geometry.…”

Direction of Arrival Estimation in the Spherical Harmonic Domain Using Subspace Pseudointensity Vectors

“…By identifying TF-regions where a single source is dominant, single source DOA estimation methods can be employed locally to those regions [12], [17], [18]. This class of methods exploits the principle that, for a single dominant source, the rank of the spatial covariance matrix is unity.…”

Real-time sound source localisation for target tracking applications using an asynchronous microphone array

“…There exists a number of examples of sound source localization, which rely on signal processing algorithms for microphone arrays to estimate the Direction Of Arrival (DOA) such as [10] [11] [12]. The signal processing algorithms for microphone arrays are capable to deal with sound source separation for automatic speech recognition and sound source localization independently.…”