  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.
