Sams-Net: A Sliced Attention-based Neural Network for Music Source Separation
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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2019
- DOI:
- 10.48550/arXiv.1909.05746
- arXiv:
- arXiv:1909.05746
- Bibcode:
- 2019arXiv190905746L
- Keywords:
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- Electrical Engineering and Systems Science - Audio and Speech Processing;
- Computer Science - Information Retrieval;
- Computer Science - Machine Learning;
- Computer Science - Sound
- E-Print:
- Submitted to Interspeech 2020