# A unified convolutional beamformer for simultaneous denoising and dereverberation

## TL;DR

This paper introduces a unified convolutional beamformer called WPD that optimally combines denoising and dereverberation, significantly enhancing speech quality and recognition accuracy.

## Contribution

It presents a novel unified beamformer that integrates dereverberation and denoising into a single optimized framework, improving over traditional sequential methods.

## Key findings

- Substantial improvement in speech enhancement metrics.
- Enhanced automatic speech recognition performance.
- Effective integration of dereverberation and denoising.

## Abstract

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.