# W-Net BF: DNN-based Beamformer Using Joint Training Approach

arXiv: https://arxiv.org/abs/1910.14262 · HTML: https://mlsanity.com/paper/1910.14262/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Blind Speech Separation and Dereverberation using Neural Beamforming](https://mlsanity.com/paper/2103.13443/) (2021)

## Abstract (arXiv)

Acoustic beamformers have been widely used to enhance audio signals. The best current methods are DNN-powered variants of the generalized eigenvalue beamformer, and DNN-based filterestimation methods that directly compute beamforming filters. Both approaches, while effective, have blindspots in their generalizability. We propose a novel approach that combines both approaches into a single framework that attempts to exploit the best features of both. The resulting model, called a W-Net beamformer, includes two components: the first computes a noise-masked reference which the second uses to estimate beamforming filters. Results on data that include a wide variety of room and noise conditions, including static and mobile noise sources, show that the proposed beamformer outperforms other methods in all tested evaluation metrics.