  Recent approaches for music source separation are almost exclusively based on
deep neural networks, mostly employing recurrent neural networks (RNNs).
Although RNNs are in many cases superior than other types of deep neural
networks for sequence processing, they are known to have specific difficulties
in training and parallelization, especially for the typically long sequences
encountered in music source separation. In this paper we present a use-case of
replacing RNNs with depth-wise separable (DWS) convolutions, which are a
lightweight and faster variant of the typical convolutions. We focus on singing
voice separation, employing an RNN architecture, and we replace the RNNs with
DWS convolutions (DWS-CNNs). We conduct an ablation study and examine the
effect of the number of channels and layers of DWS-CNNs on the source
separation performance, by utilizing the standard metrics of
signal-to-artifacts, signal-to-interference, and signal-to-distortion ratio.
Our results show that by replacing RNNs with DWS-CNNs yields an improvement of
1.20, 0.06, 0.37 dB, respectively, while using only 20.57% of the amount of
parameters of the RNN architecture.
