  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.
