  Time-domain audio separation network (TasNet) has achieved remarkable
performance in blind source separation (BSS). Classic multi-channel speech
processing framework employs signal estimation and beamforming. For example,
Beam-TasNet links multi-channel convolutional TasNet (MC-Conv-TasNet) with
minimum variance distortionless response (MVDR) beamforming, which leverages
the strong modeling ability of data-driven network and boosts the performance
of beamforming with an accurate estimation of speech statistics. Such
integration can be viewed as a directed acyclic graph by accepting
multi-channel input and generating multi-source output. In this paper, we
design a "multi-channel input, multi-channel multi-source output" (MIMMO)
speech separation system entitled "Beam-Guided TasNet", where MC-Conv-TasNet
and MVDR can interact and promote each other more compactly under a directed
cyclic flow. Specifically, the first stage uses Beam-TasNet to generate
estimated single-speaker signals, which favors the separation in the second
stage. The proposed framework facilitates iterative signal refinement with the
guide of beamforming and seeks to reach the upper bound of the MVDR-based
methods. Experimental results on the spatialized WSJ0-2MIX demonstrate that the
Beam-Guided TasNet has achieved an SDR of 21.5 dB, exceeding the baseline
Beam-TasNet by 4.1 dB under the same model size and narrowing the gap with the
oracle signal-based MVDR to 2 dB.
