DBNET: DOA-driven beamforming network for end-to-end farfield sound source separation

TL;DR

This paper introduces DBnet, an end-to-end deep learning framework that combines DOA estimation and beamforming for effective far-field sound source separation in noisy, reverberant environments, outperforming existing methods.

Contribution

The paper presents a novel DOA-driven beamforming network trained without ground-truth DOAs and extends it with post masking for improved separation performance.

Findings

Outperforms state-of-the-art source separation methods

Effective in reverberant and noisy environments

End-to-end training without ground-truth DOAs

Abstract

Many deep learning techniques are available to perform source separation and reduce background noise. However, designing an end-to-end multi-channel source separation method using deep learning and conventional acoustic signal processing techniques still remains challenging. In this paper we propose a direction-of-arrival-driven beamforming network (DBnet) consisting of direction-of-arrival (DOA) estimation and beamforming layers for end-to-end source separation. We propose to train DBnet using loss functions that are solely based on the distances between the separated speech signals and the target speech signals, without a need for the ground-truth DOAs of speakers. To improve the source separation performance, we also propose end-to-end extensions of DBnet which incorporate post masking networks. We evaluate the proposed DBnet and its extensions on a very challenging dataset, targeting realistic far-field sound source separation in reverberant and noisy environments. The experimental results show that the proposed extended DBnet using a convolutional-recurrent post masking network outperforms state-of-the-art source separation methods.