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

Tingle Li; Jiawei Chen; Haowen Hou; Ming Li

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arXiv · 1909.05746

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

Tingle Li·Jiawei Chen·Haowen Hou·Ming Li

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Abstract

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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BibTeX RIS

Tingle Li, Jiawei Chen, Haowen Hou, Ming Li. 2019-09-12. Sams-Net: A Sliced Attention-based Neural Network for Music Source Separation. https://arxiv.org/abs/1909.05746

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