A Unified Convolutional Beamformer for Simultaneous Denoising and Dereverberation

摘要

This letter proposes a method for estimating a convolutional beamformer that can perform denoising and dereverberation simultaneously in an optimal way. The application of dereverberation based on a weighted prediction error (WPE) method followed by denoising based on a minimum variance distortionless response (MVDR) beamformer has conventionally been considered a promising approach, however, the optimality of this approach cannot be guaranteed. To realize the optimal integration of denoising and dereverberation, we present a method that unifies the WPE dereverberation method and a variant of the MVDR beamformer, namely a minimum power distortionless response beamformer, into a single convolutional beamformer, and we optimize it based on a single unified optimization criterion. The proposed beamformer is referred to as a weighted power minimization distortionless response beamformer. Experiments show that the proposed method substantially improves the speech enhancement performance in terms of both objective speech enhancement measures and automatic speech recognition performance.