Sams-Net: A Sliced Attention-based Neural Network for Music Source Separation

Recent studies in deep learning-based source separation have two major approaches: one approach is modeling in the spectrogram domain, and the other approach is modeling in the time domain, but all of them used pure CNN or LSTM. In this paper, we propose a Sliced Attention-based neural network (Sams-Net) at the spectrogram domain for music source separation task, which enables feature interactions from the magnitude spectrogram contribute differently to the separation. Sams-Net has two main advantages: one is that it can be easily parallel computing compared with LSTM, and the other is that it has a larger receptive field compared with CNN. Experiments indicate that our proposed Sams-Net outperforms most of the state-of-the-art methods, although it contains fewer parameters.