  Deep learning speech separation algorithms have achieved great success in
improving the quality and intelligibility of separated speech from mixed audio.
Most previous methods focused on generating a single-channel output for each of
the target speakers, hence discarding the spatial cues needed for the
localization of sound sources in space. However, preserving the spatial
information is important in many applications that aim to accurately render the
acoustic scene such as in hearing aids and augmented reality (AR). Here, we
propose a speech separation algorithm that preserves the interaural cues of
separated sound sources and can be implemented with low latency and high
fidelity, therefore enabling a real-time modification of the acoustic scene.
Based on the time-domain audio separation network (TasNet), a single-channel
time-domain speech separation system that can be implemented in real-time, we
propose a multi-input-multi-output (MIMO) end-to-end extension of TasNet that
takes binaural mixed audio as input and simultaneously separates target
speakers in both channels. Experimental results show that the proposed
end-to-end MIMO system is able to significantly improve the separation
performance and keep the perceived location of the modified sources intact in
various acoustic scenes.
