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A Generalized Subspace Approach for Multichannel Speech Enhancement Using Machine Learning-Based Speech Presence Probability Estimation

A generalized subspace-based multichannel speech enhancement in frequency domain is proposed by estimating multichannel speech presence probability using machine learning methods. An efficient and low-latency neural networks (NN) model is introduced to discriminatively learn a gain mask for separating the speech and the noise components in noisy scenarios. Besides, a generalized subspace-based approach in frequency domain is proposed, where the speech power spectral density (PSD) matrix and the noise PSD matrix are estimated by short-term and long-term averaging periods, respectively. Experimental results show that the proposed method outperforms the existing NN-based beamforming methods in terms of the perceptual evaluation of speech quality score and the segmental signal-to-noise ratio improvement.

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AES - Audio Engineering Society