# CASS: Cross Adversarial Source Separation via Autoencoder

arXiv: https://arxiv.org/abs/1905.09877 · HTML: https://mlsanity.com/paper/1905.09877/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 3 of 3)

- [IAE-Net: Integral Autoencoders for Discretization-Invariant Learning](https://mlsanity.com/paper/2203.05142/) (2022)
- [Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation](https://mlsanity.com/paper/2101.00701/) (2021)
- [Blind Source Separation of Single-Channel Mixtures via Multi-Encoder Autoencoders](https://mlsanity.com/paper/2309.07138/) (2023)

## Abstract (arXiv)

This paper introduces a cross adversarial source separation (CASS) framework via autoencoder, a new model that aims at separating an input signal consisting of a mixture of multiple components into individual components defined via adversarial learning and autoencoder fitting. CASS unifies popular generative networks like auto-encoders (AEs) and generative adversarial networks (GANs) in a single framework. The basic building block that filters the input signal and reconstructs the i-th target component is a pair of deep neural networks ENi and DEi as an encoder for dimension reduction and a decoder for component reconstruction, respectively. The decoder DEi as a generator is enhanced by a discriminator network Di that favors signal structures of the i-th component in the i-th given dataset as guidance through adversarial learning. In contrast with existing practices in AEs which trains each Auto-Encoder independently, or in GANs that share the same generator, we introduce cross adversarial training that emphasizes adversarial relation between any arbitrary network pairs (DEi,Dj), achieving state-of-the-art performance especially when target components share similar data structures.