Explainable DNN-based Beamformer with Postfilter

TL;DR

This paper presents an explainable deep neural network-based beamformer with a postfilter that combines spatial filtering and attention mechanisms, providing improved speech enhancement with better interpretability and no need for prior speaker activity knowledge.

Contribution

It introduces a novel two-stage beamforming approach with an integrated attention mechanism and spatial analysis, advancing explainability and performance in multichannel speech processing.

Findings

Superior speech enhancement results without prior speaker activity knowledge

Effective integration of attention mechanism improves noise suppression

Thorough spatial analysis enhances understanding of network performance

Abstract

This paper introduces an explainable DNN-based beamformer with a postfilter (ExNet-BF+PF) for multichannel signal processing. Our approach combines the U-Net network with a beamformer structure to address this problem. The method involves a two-stage processing pipeline. In the first stage, time-invariant weights are applied to construct a multichannel spatial filter, namely a beamformer. In the second stage, a time-varying single-channel post-filter is applied at the beamformer output. Additionally, we incorporate an attention mechanism inspired by its successful application in noisy and reverberant environments to improve speech enhancement further. Furthermore, our study fills a gap in the existing literature by conducting a thorough spatial analysis of the network's performance. Specifically, we examine how the network utilizes spatial information during processing. This analysis yields valuable insights into the network's functionality, thereby enhancing our understanding of its overall performance. Experimental results demonstrate that our approach is not only straightforward to train but also yields superior results, obviating the necessity for prior knowledge of the speaker's activity.