Globally optimized least-squares post-filtering for microphone array speech enhancement

Abstract

Existing post-filtering techniques for microphone array speech enhancement have two common deficiencies. First, they assume that the noise is either white or diffuse and cannot deal with point inter-ferers. Second, they estimate the post-filter coefficients using only two microphones at a time and then perform averaging over all microphone pairs, yielding a suboptimal solution at best. In this paper, we present a novel post-filtering algorithm that alleviates the first limitation by using a more generalized signal model including not only white and diffuse but also point interferers, and overcomes the second deficiency by offering a globally optimized least-squares solution over all microphones. It is shown by simulations that the proposed method outperforms the existing algorithms in many different acoustic scenarios.