# A unified convolutional beamformer for simultaneous denoising and dereverberation

arXiv: https://arxiv.org/abs/1812.08400 · HTML: https://mlsanity.com/paper/1812.08400/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition](https://mlsanity.com/paper/2307.02909/) (2023)
- [End-to-End Multi-speaker ASR with Independent Vector Analysis](https://mlsanity.com/paper/2204.00218/) (2022)
- [Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments](https://mlsanity.com/paper/2204.02741/) (2022)
- [Blind Speech Separation and Dereverberation using Neural Beamforming](https://mlsanity.com/paper/2103.13443/) (2021)
- [Run-Time Adaptation of Neural Beamforming for Robust Speech Dereverberation and Denoising](https://mlsanity.com/paper/2410.22805/) (2024)

## Abstract (arXiv)

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.