Perpendicular Cross-Spectra Fusion for Sound Source Localization With a Planar Microphone Array

Abstract

Multiple sound source localization in reverberant environments stands as one of the most difficult challenges for many applications related to microphone array signal processing. In this paper, we describe perpendicular cross-spectra fusion (PCSF), a new direction-of-arrival (DOA) estimation algorithm, which utilizes an analytic formula for direction estimation in the time–frequency (TF) domain. Inherent to this technique is the presence of multiple direction estimation subsystems which operate in parallel, producing a multiplicity of candidate DOAs at each TF point. We define a metric of coherence, based on the property of divergence of the different DOA estimators, for assessing the reliability of different signal portions, so that only TF bins with a high quality of directional information are exploited for local DOA estimation. The resulting collection of local DOAs is provided as input to a recently proposed histogram processing approach, which is based on matching pursuit. Results based on simulation and real recordings illustrate the advantages of PCSF compared to other DOA estimation techniques subject to the same histogram-based processing, in the context of real-time multiple source localization and counting; improved performance in reverberant conditions and high tolerance to diffuse and common mode noise.