  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.
