A robust sound-source separation algorithm for an adverse environment that combines MVDR-PHAT with the CASA framework
WASPAA(2011)
摘要
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.
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关键词
speech processing,proven-robust phase transform,beamforming,array signal processing,robust sound-source separation algorithm,casa framework,reverberation noise,acoustic arrays,blind source separation,computational auditory scene analysis,background noise,microphone arrays,null-steering beamformer minimum-variance distortionless response,transforms,audio signal processing,microphone-array processing,blind-source separation,speech enhancement,mvdr-phat,acoustic beam steering,high-quality speech signal extraction,human hearing perception,spectrogram,speech,time frequency analysis
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