Globally Optimized Least-Squares Post-Filtering For Microphone Array Speech Enhancement

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 interferers. 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.