  Music source separation has been a popular topic in signal processing for
decades, not only because of its technical difficulty, but also due to its
importance to many commercial applications, such as automatic karoake and
remixing. In this work, we propose a novel self-attention network to separate
voice and accompaniment in music. First, a convolutional neural network (CNN)
with densely-connected CNN blocks is built as our base network. We then insert
self-attention subnets at different levels of the base CNN to make use of the
long-term intra-dependency of music, i.e., repetition. Within self-attention
subnets, repetitions of the same musical patterns inform reconstruction of
other repetitions, for better source separation performance. Results show the
proposed method leads to 19.5% relative improvement in vocals separation in
terms of SDR. We compare our methods with state-of-the-art systems i.e.
MMDenseNet and MMDenseLSTM.
