  Speech separation models are used for isolating individual speakers in many
speech processing applications. Deep learning models have been shown to lead to
state-of-the-art (SOTA) results on a number of speech separation benchmarks.
One such class of models known as temporal convolutional networks (TCNs) has
shown promising results for speech separation tasks. A limitation of these
models is that they have a fixed receptive field (RF). Recent research in
speech dereverberation has shown that the optimal RF of a TCN varies with the
reverberation characteristics of the speech signal. In this work deformable
convolution is proposed as a solution to allow TCN models to have dynamic RFs
that can adapt to various reverberation times for reverberant speech
separation. The proposed models are capable of achieving an 11.1 dB average
scale-invariant signalto-distortion ratio (SISDR) improvement over the input
signal on the WHAMR benchmark. A relatively small deformable TCN model of 1.3M
parameters is proposed which gives comparable separation performance to larger
and more computationally complex models.
