  Most existing deep learning based binaural speaker separation systems focus
on producing a monaural estimate for each of the target speakers, and thus do
not preserve the interaural cues, which are crucial for human listeners to
perform sound localization and lateralization. In this study, we address
talker-independent binaural speaker separation with interaural cues preserved
in the estimated binaural signals. Specifically, we extend a newly-developed
gated recurrent neural network for monaural separation by additionally
incorporating self-attention mechanisms and dense connectivity. We develop an
end-to-end multiple-input multiple-output system, which directly maps from the
binaural waveform of the mixture to those of the speech signals. The
experimental results show that our proposed approach achieves significantly
better separation performance than a recent binaural separation approach. In
addition, our approach effectively preserves the interaural cues, which
improves the accuracy of sound localization.
