# One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal

arXiv: https://arxiv.org/abs/2110.06568 · HTML: https://mlsanity.com/paper/2110.06568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Dual Learning for Machine Translation](https://mlsanity.com/paper/1611.00179/) (2016)
- [Generative Adversarial Source Separation](https://mlsanity.com/paper/1710.10779/) (2017)
- ['Less Than One'-Shot Learning: Learning N Classes From M<N Samples](https://mlsanity.com/paper/2009.08449/) (2020)
- [Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks](https://mlsanity.com/paper/1906.07552/) (2019)
- [Single Channel Audio Source Separation using Convolutional Denoising Autoencoders](https://mlsanity.com/paper/1703.08019/) (2017)
- [SVSGAN: Singing Voice Separation via Generative Adversarial Network](https://mlsanity.com/paper/1710.11428/) (2017)
- [Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation](https://mlsanity.com/paper/2008.00816/) (2020)

## Abstract (arXiv)

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on separating two sources and have weak generalization performance. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to realize one-to-multiple cross-domain mapping. This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model. Besides, one-to-multiple datasets are created which including the mixtures and corresponding sources for this study. The experiment was carried out on four different datasets and tested with signals mixed in different proportions. Experimental results show that the proposed algorithm can achieve high performance in peak signal-to-noise ratio (PSNR) and correlation, which outperforms state-of-the-art algorithms.