  Robust speech processing in multi-talker environments requires effective
speech separation. Recent deep learning systems have made significant progress
toward solving this problem, yet it remains challenging particularly in
real-time, short latency applications. Most methods attempt to construct a mask
for each source in time-frequency representation of the mixture signal which is
not necessarily an optimal representation for speech separation. In addition,
time-frequency decomposition results in inherent problems such as
phase/magnitude decoupling and long time window which is required to achieve
sufficient frequency resolution. We propose Time-domain Audio Separation
Network (TasNet) to overcome these limitations. We directly model the signal in
the time-domain using an encoder-decoder framework and perform the source
separation on nonnegative encoder outputs. This method removes the frequency
decomposition step and reduces the separation problem to estimation of source
masks on encoder outputs which is then synthesized by the decoder. Our system
outperforms the current state-of-the-art causal and noncausal speech separation
algorithms, reduces the computational cost of speech separation, and
significantly reduces the minimum required latency of the output. This makes
TasNet suitable for applications where low-power, real-time implementation is
desirable such as in hearable and telecommunication devices.
