Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix

TL;DR

This paper introduces a novel adversarial approach for unsupervised single-channel blind source separation, leveraging source independence constraints to improve separation quality, validated on image sources.

Contribution

It presents the first use of adversarial training for blind source separation, exploiting source independence for effective separation without supervision.

Findings

Effective separation demonstrated on image sources

Adversarial constraints improve source independence

Promising results for general signal separation

Abstract

Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression tasks. The power of adversarial training however has not yet been realized for blind source separation tasks. In this work, we propose a novel method for blind source separation (BSS) using adversarial methods. We rely on the independence of sources for creating adversarial constraints on pairs of approximately separated sources, which ensure good separation. Experiments are carried out on image sources validating the good performance of our approach, and presenting our method as a promising approach for solving BSS for general signals.