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

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.

arXiv comment5 pages, 4 figures

References in corpus (5)

• Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
• Deep attractor network for single-microphone speaker separation
• Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals
• FaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing
• FurcaNet: An end-to-end deep gated convolutional, long short-term memory, deep neural networks for single channel speech separation

Cited by in corpus (4)

• L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment
• Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
• L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing
• Speaker-conditioned Target Speaker Extraction based on Customized LSTM Cells