Globally optimized least-squares post-filtering for speech enhancement

Abstract

Existing post-filtering methods for microphone array speech enhancement have two common deficiencies. First, they assume that 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, performing averaging over all the microphones pairs, yielding a suboptimal solution. The provided method describes a post-filtering solution that implements signal models which handle white noise, diffuse noise, and point interferers. The method also implements a globally optimized least-squares approach of microphones in a microphone array, providing a more optimal solution than existing conventional methods. Experimental results demonstrate the described method outperforming conventional methods in various acoustic scenarios.