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

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

12 Sep 2019arXiv:1909.05746archive 2025-07-28

Tingle Li, Jia-Wei Chen, Haowen Hou, Ming Li

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.

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