  This paper addresses the problem of multi-channel multi-speech separation
based on deep learning techniques. In the short time Fourier transform domain,
we propose an end-to-end narrow-band network that directly takes as input the
multi-channel mixture signals of one frequency, and outputs the separated
signals of this frequency. In narrow-band, the spatial information (or
inter-channel difference) can well discriminate between speakers at different
positions. This information is intensively used in many narrow-band speech
separation methods, such as beamforming and clustering of spatial vectors. The
proposed network is trained to learn a rule to automatically exploit this
information and perform speech separation. Such a rule should be valid for any
frequency, thence the network is shared by all frequencies. In addition, a
full-band permutation invariant training criterion is proposed to solve the
frequency permutation problem encountered by most narrow-band methods.
Experiments show that, by focusing on deeply learning the narrow-band
information, the proposed method outperforms the oracle beamforming method and
the state-of-the-art deep learning based method.
