  Stacked dilated convolutions used in Wavenet have been shown effective for
generating high-quality audios. By replacing pooling/striding with dilation in
convolution layers, they can preserve high-resolution information and still
reach distant locations. Producing high-resolution predictions is also crucial
in music source separation, whose goal is to separate different sound sources
while maintaining the quality of the separated sounds. Therefore, this paper
investigates using stacked dilated convolutions as the backbone for music
source separation. However, while stacked dilated convolutions can reach wider
context than standard convolutions, their effective receptive fields are still
fixed and may not be wide enough for complex music audio signals. To reach
information at remote locations, we propose to combine dilated convolution with
a modified version of gated recurrent units (GRU) called the `Dilated GRU' to
form a block. A Dilated GRU unit receives information from k steps before
instead of the previous step for a fixed k. This modification allows a GRU unit
to reach a location with fewer recurrent steps and run faster because it can
execute partially in parallel. We show that the proposed model with a stack of
such blocks performs equally well or better than the state-of-the-art models
for separating vocals and accompaniments.
