  Deep attractor networks (DANs) perform speech separation with discriminative
embeddings and speaker attractors. Compared with methods based on the
permutation invariant training (PIT), DANs define a deep embedding space and
deliver a more elaborate representation on each time-frequency (T-F) bin.
However, it has been observed that the DANs achieve limited improvement on the
signal quality if directly deployed in a reverberant environment. Following the
success of time-domain separation networks on the clean mixture speech, we
propose a time-domain DAN (TD-DAN) with two-streams of convolutional networks,
which efficiently perform both dereverberation and separation tasks under the
condition of a variable number of speakers. The speaker encoding stream (SES)
of the TD-DAN is trained to model the speaker information in the embedding
space. The speech decoding stream (SDS) accepts speaker attractors from the SES
and learns to estimate early reflections from the spectro-temporal
representations. Meanwhile, additional clustering losses are used to bridge the
gap between the oracle and the estimated attractors. Experiments were conducted
on the Spatialized Multi-Speaker Wall Street Journal (SMS-WSJ) dataset. The
early reflection was compared with the anechoic and reverberant signals and
then was chosen as the learning targets. The experimental results demonstrated
that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR)
gains of 9.79/7.47 dB on the reverberant 2/3-speaker evaluation set, exceeding
the baseline DAN and convolutional time-domain audio separation network
(Conv-TasNet) by 1.92/0.68 dB and 0.91/0.47 dB, respectively.
