The end-to-end approach for single-channel speech separation has been studied
recently and shown promising results. This paper extended the previous approach
and proposed a new end-to-end model for multi-channel speech separation. The
primary contributions of this work include 1) an integrated waveform-in
waveform-out separation system in a single neural network architecture. 2) We
reformulate the traditional short time Fourier transform (STFT) and
inter-channel phase difference (IPD) as a function of time-domain convolution
with a special kernel. 3) We further relaxed those fixed kernels to be
learnable, so that the entire architecture becomes purely data-driven and can
be trained from end-to-end. We demonstrate on the WSJ0 far-field speech
separation task that, with the benefit of learnable spatial features, our
proposed end-to-end multi-channel model significantly improved the performance
of previous end-to-end single-channel method and traditional multi-channel
methods.

本文提出了一种基于端到端的多通道语音分离模型，通过可学习空间特征的提出，在 WSJ0 远场语音分离任务中显著改进了单通道和传统多通道方法的性能。