  Music source separation involves a large input field to model a long-term
dependence of an audio signal. Previous convolutional neural network
(CNN)-based approaches address the large input field modeling using
sequentially down- and up-sampling feature maps or dilated convolution. In this
paper, we claim the importance of a rapid growth of a receptive field and a
simultaneous modeling of multi-resolution data in a single convolution layer,
and propose a novel CNN architecture called densely connected dilated DenseNet
(D3Net). D3Net involves a novel multi-dilated convolution that has different
dilation factors in a single layer to model different resolutions
simultaneously. By combining the multi-dilated convolution with DenseNet
architecture, D3Net avoids the aliasing problem that exists when we naively
incorporate the dilated convolution in DenseNet. Experimental results on
MUSDB18 dataset show that D3Net achieves state-of-the-art performance with an
average signal to distortion ratio (SDR) of 6.01 dB.
