  Convolutional Neural Network (CNN) or Long short-term memory (LSTM) based
models with the input of spectrogram or waveforms are commonly used for deep
learning based audio source separation. In this paper, we propose a Sliced
Attention-based neural network (Sams-Net) in the spectrogram domain for the
music source separation task. It enables spectral feature interactions with
multi-head attention mechanism, achieves easier parallel computing and has a
larger receptive field compared with LSTMs and CNNs respectively. Experimental
results on the MUSDB18 dataset show that the proposed method, with fewer
parameters, outperforms most of the state-of-the-art DNN-based methods.
