  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.
