Voice and accompaniment separation in music using self-attention convolutional neural network

Understand

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.