  In this paper, we propose a multi-channel network for simultaneous speech
dereverberation, enhancement and separation (DESNet). To enable gradient
propagation and joint optimization, we adopt the attentional selection
mechanism of the multi-channel features, which is originally proposed in
end-to-end unmixing, fixed-beamforming and extraction (E2E-UFE) structure.
Furthermore, the novel deep complex convolutional recurrent network (DCCRN) is
used as the structure of the speech unmixing and the neural network based
weighted prediction error (WPE) is cascaded beforehand for speech
dereverberation. We also introduce the staged SNR strategy and symphonic loss
for the training of the network to further improve the final performance.
Experiments show that in non-dereverberated case, the proposed DESNet
outperforms DCCRN and most state-of-the-art structures in speech enhancement
and separation, while in dereverberated scenario, DESNet also shows
improvements over the cascaded WPE-DCCRN networks.
