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

arXiv: https://arxiv.org/abs/2001.10190 · HTML: https://mlsanity.com/paper/2001.10190/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Upsampling layers for music source separation](https://mlsanity.com/paper/2111.11773/) (2021)
- [Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method](https://mlsanity.com/paper/2105.04079/) (2021)
- [Adaptive Frequency Enhancement Network for Single Image Deraining](https://mlsanity.com/paper/2407.14292/) (2024)
- [Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs](https://mlsanity.com/paper/2502.15285/) (2025)

## Abstract (arXiv)

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.