Multiple Sound Source Counting and Localization Based on TF-Wise Spatial Spectrum Clustering

Abstract

This paper addresses the problem of multiple sound source counting and localization in adverse acoustic environments, using microphone array recordings. The proposed time-frequency TF wise spatial spectrum clustering based method contains two stages. First, given the received sensor signals, the spatial correlation matrix is computed and denoised in the TF domain. The TF-wise spatial spectrum is estimated based on the signal subspace information, and further enhanced by an exponential transform, which can increase the reliability of the source presence possibility reflected by spatial spectrum. Second, to jointly count and localize sound sources, the enhanced TF-wise spatial spectra are divided into several clusters with each cluster corresponding to one source. Sources are successively detected by searching the significant peaks of the remaining global spatial spectrum, which is formed using unassigned spatial spectra. After each new source detection, spatial spectra are reassigned to detected sources according to the dominance association between them. The interaction between sources is reduced by iteratively performing new source detection and spatial spectrum assignment. Experiments on both simulated data and real-world data demonstrate the superiority of the proposed method for multiple sound source counting and localization in the environment with different levels of noise and reverberation.