Microphone array post-filter based on accurate estimation of noise power spectral density

The conventional post-filtering methods in the multi-channel noise reduction application only rely on multi-channel input signals while neglecting the noise reduction capability of the microphone array beamformer, which results in overestimation of the noise power spectral density (PSD) and consequently suboptimal filters in the minimum mean-square-error (MMSE) sense. This paper proposes a novel microphone array post-filter based on accurate PSD estimation, the beamformer output in the microphone array is used to estimate the noise PSD, and a two-step noise reduction method is also employed to obtain accurate post-filter gain function. The error analysis is also given to highlight the advantage of the proposed algorithm over the conventional Zelinski and McCowan post-filters. The performance advantages of the proposed post-filter are demonstrated in terms of segmental SNR (SegSNR), short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and deep noise suppression mean opinion score (DNSMOS).