Generative Adversarial Source Separation

Y. Cem Sübakan; Paris Smaragdis

ICASSP 2018accepted0 citations

Generative Adversarial Source Separation

Abstract

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 multilayer 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.

BibTeX

@inproceedings{icassp2018_generativeadvers,
  title = {Generative Adversarial Source Separation},
  author = {Y. Cem Sübakan and Paris Smaragdis},
  booktitle = {ICASSP 2018},
  year = {2018}
}