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      Time-Domain Audio Source Separation Based on Wave-U-Net Combined with Discrete Wavelet Transform

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          Abstract

          We propose a time-domain audio source separation method using down-sampling (DS) and up-sampling (US) layers based on a discrete wavelet transform (DWT). The proposed method is based on one of the state-of-the-art deep neural networks, Wave-U-Net, which successively down-samples and up-samples feature maps. We find that this architecture resembles that of multiresolution analysis, and reveal that the DS layers of Wave-U-Net cause aliasing and may discard information useful for the separation. Although the effects of these problems may be reduced by training, to achieve a more reliable source separation method, we should design DS layers capable of overcoming the problems. With this belief, focusing on the fact that the DWT has an anti-aliasing filter and the perfect reconstruction property, we design the proposed layers. Experiments on music source separation show the efficacy of the proposed method and the importance of simultaneously considering the anti-aliasing filters and the perfect reconstruction property.

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          Author and article information

          Journal
          28 January 2020
          Article
          2001.10190
          23c2e022-5291-4963-9af6-737165899e3d

          http://creativecommons.org/licenses/by-nc-sa/4.0/

          History
          Custom metadata
          5 pages, to appear in IEEE International Conference on Acoustics, Speech, and Signal Processing 2020 (ICASSP 2020)
          cs.SD cs.LG cs.MM eess.AS

          Artificial intelligence,Electrical engineering,Graphics & Multimedia design

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