Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix
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Unsupervised single-channel blind 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 prior-free signal regression tasks. The power of adversarial training however has not yet been realized for unsupervised source separation tasks. In this work, we propose a novel method for unsupervised 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 unsupervised BSS for general signals.
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