A robust sound-source separation algorithm for an adverse environment that combines MVDR-PHAT with the CASA framework

Extracting a high-quality speech signal of a single source from a multiple-source input in an adverse environment has always been a challenge for microphone-array processing. Three major approaches have been proposed to tackle this problem: blind-source separation (BSS), beamforming (BF), and computational auditory scene analysis (CASA). Combinations of the CASA and BF, BSS and BF also have been introduced. In this paper, we propose a new algorithm which utilizes the null-steering beamformer minimum-variance distortionless response (MVDR) using the proven-robust phase transform (MVDR-PHAT) and the CASA framework that closely mimics human hearing perception. Experimental results using real data recorded in a room with high background and reverberation noise indicated the improved performance of the proposed algorithm compared to those of traditional beamforming algorithms and an SRP-PHAT-based source-separation algorithm recently described at ICASSP 2010.