A microphone array system integrating beamforming, feature enhancement, and spectral mask-based noise estimation

This paper proposes a microphone array system that integrates beamforming, feature enhancement, and highly accurate noise feature model estimation based on spectral masking. Previously proposed methods for combining beamformers and single-channel post-filters estimate noise power spectra or noise features based only on spatial information acquired from multiple microphones. These methods suffer from low noise estimation accuracy when the available microphones are limited or when there are array calibration or steering vector estimation errors. By contrast, the proposed method estimates a noise feature model accurately in a highly adaptive way by capitalizing on both spatial information and the characteristics of speech. Specifically, the method leverages an inter-microphone phase difference model, a clean feature model, and a harmonicity-based spectral mask model for the accurate estimation of spectral masks, each of which indicates the presence or absence of speech at a particular frequency bin. The estimated spectral masks are used to obtain the time-varying noise feature model. Results of a digit recognition experiment prove that the proposed system significantly outperforms an existing microphone array system combining a beamformer and a post-filter.