# Generative Adversarial Source Separation

arXiv: https://arxiv.org/abs/1710.10779 · HTML: https://mlsanity.com/paper/1710.10779/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2017.

## Built on by (most cited first, 19 of 19)

- [Multi-Source Diffusion Models for Simultaneous Music Generation and Separation](https://mlsanity.com/paper/2302.02257/) (2023)
- [Diffusion-based Generative Speech Source Separation](https://mlsanity.com/paper/2210.17327/) (2022)
- [Simultaneous Music Separation and Generation Using Multi-Track Latent Diffusion Models](https://mlsanity.com/paper/2409.12346/) (2024)
- [Latent Autoregressive Source Separation](https://mlsanity.com/paper/2301.08562/) (2023)
- [Adversarial Permutation Invariant Training for Universal Sound Separation](https://mlsanity.com/paper/2210.12108/) (2022)
- [Source Separation by Flow Matching](https://mlsanity.com/paper/2505.16119/) (2025)
- [ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior](https://mlsanity.com/paper/2505.05657/) (2025)
- [MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction](https://mlsanity.com/paper/2505.23305/) (2025)
- [User-guided Generative Source Separation](https://mlsanity.com/paper/2507.01339/) (2025)
- [Distribution Preserving Source Separation With Time Frequency Predictive Models](https://mlsanity.com/paper/2303.05896/) (2023)
- [Improving Music Source Separation with Diffusion and Consistency Refinement](https://mlsanity.com/paper/2412.06965/) (2024)
- [Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images](https://mlsanity.com/paper/2102.06802/) (2021)
- [One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal](https://mlsanity.com/paper/2110.06568/) (2021)
- [Probabilistic Modelling of Signal Mixtures with Differentiable Dictionaries](https://mlsanity.com/paper/2211.15439/) (2022)
- [Differentiable Dictionary Search: Integrating Linear Mixing with Deep Non-Linear Modelling for Audio Source Separation](https://mlsanity.com/paper/2211.15524/) (2022)
- [Adversarial Generative NMF for Single Channel Source Separation](https://mlsanity.com/paper/2305.01758/) (2023)
- [Maximum Discrepancy Generative Regularization and Non-Negative Matrix Factorization for Single Channel Source Separation](https://mlsanity.com/paper/2404.15296/) (2024)
- [Source Separation of Multi-source Raw Music using a Residual Quantized Variational Autoencoder](https://mlsanity.com/paper/2408.07020/) (2024)
- [SURF: Separation via Unsupervised Remixing Flow](https://mlsanity.com/paper/2606.04921/) (2026)

## Abstract (arXiv)

Generative source separation methods such as non-negative matrix factorization (NMF) or auto-encoders, rely on the assumption of an output probability density. Generative Adversarial Networks (GANs) can learn data distributions without needing a parametric assumption on the output density. We show on a speech source separation experiment that, a multi-layer perceptron trained with a Wasserstein-GAN formulation outperforms NMF, auto-encoders trained with maximum likelihood, and variational auto-encoders in terms of source to distortion ratio.