Multiple Sound Source Localization Using SVD-PHAT-ATV on Blind Source Separation

Abstract

Estimating the location of a target sound source is an important task in many fields. The blind source separation (BSS) algorithm is closely related to the multiple sound source localization problem. A convolutional beamformer (CBF), one of the BSS algorithms, can successfully estimate the acoustic transfer vector (ATV) for each of the target sources which contains spatial information on the corresponding source. In this paper, we propose an algorithm called SVD-PHAT-ATV that estimates the directions of arrival (DoAs) of multiple sound sources using the ATVs of the CBF. In particular, we extract the DoAs of the target sources from the ATVs by employing the singular value decomposition (SVD) of the steered-response power (SRP) weight matrix. Experimental results on simulation data showed that the proposed method demonstrated better performance and lower computational complexity than the existing BSS-based multiple sound source localization algorithms. In experiments on real-recorded data, the proposed method outperformed the existing first-place algorithm using a robot head microphone array in Task 2 of the LOCATA Challenge.