A unified convolutional beamformer for simultaneous denoising and dereverberation
This addresses speech enhancement for applications like automatic speech recognition, but it is incremental as it builds on existing WPE and MVDR/MPDR methods.
The paper tackled the problem of suboptimal sequential denoising and dereverberation in speech enhancement by proposing a unified convolutional beamformer that integrates these tasks into a single optimization, resulting in substantial improvements in objective measures and ASR performance.
This paper 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 (MPDR) 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 (WPD) 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 (ASR) performance.