  This work proposes a multichannel speech separation method with narrow-band
Conformer (named NBC). The network is trained to learn to automatically exploit
narrow-band speech separation information, such as spatial vector clustering of
multiple speakers. Specifically, in the short-time Fourier transform (STFT)
domain, the network processes each frequency independently, and is shared by
all frequencies. For one frequency, the network inputs the STFT coefficients of
multichannel mixture signals, and predicts the STFT coefficients of separated
speech signals. Clustering of spatial vectors shares a similar principle with
the self-attention mechanism in the sense of computing the similarity of
vectors and then aggregating similar vectors. Therefore, Conformer would be
especially suitable for the present problem. Experiments show that the proposed
narrow-band Conformer achieves better speech separation performance than other
state-of-the-art methods by a large margin.
