  We propose TF-GridNet, a novel multi-path deep neural network (DNN) operating
in the time-frequency (T-F) domain, for monaural talker-independent speaker
separation in anechoic conditions. The model stacks several multi-path blocks,
each consisting of an intra-frame spectral module, a sub-band temporal module,
and a full-band self-attention module, to leverage local and global
spectro-temporal information for separation. The model is trained to perform
complex spectral mapping, where the real and imaginary (RI) components of the
input mixture are stacked as input features to predict target RI components.
Besides using the scale-invariant signal-to-distortion ratio (SI-SDR) loss for
model training, we include a novel loss term to encourage separated sources to
add up to the input mixture. Without using dynamic mixing, we obtain 23.4 dB
SI-SDR improvement (SI-SDRi) on the WSJ0-2mix dataset, outperforming the
previous best by a large margin.
