  Speech separation has been studied widely for single-channel close-talk
microphone recordings over the past few years; developed solutions are mostly
in frequency-domain. Recently, a raw audio waveform separation network (TasNet)
is introduced for single-channel data, with achieving high Si-SNR
(scale-invariant source-to-noise ratio) and SDR (source-to-distortion ratio)
comparing against the state-of-the-art solution in frequency-domain. In this
study, we incorporate effective components of the TasNet into a
frequency-domain separation method. We compare both for alternative scenarios.
We introduce a solution for directly optimizing the separation criterion in
frequency-domain networks. In addition to speech separation objective and
subjective measurements, we evaluate the separation performance on a speech
recognition task as well. We study the speech separation problem for far-field
data (more similar to naturalistic audio streams) and develop multi-channel
solutions for both frequency and time-domain separators with utilizing
spectral, spatial and speaker location information. For our experiments, we
simulated multi-channel spatialized reverberate WSJ0-2mix dataset. Our
experimental results show that spectrogram separation can achieve competitive
performance with better network design. Multi-channel framework as well is
shown to improve the single-channel performance relatively up to +35.5% and
+46% in terms of WER and SDR, respectively.
