Generative Adversarial Source Separation
نویسندگان
چکیده
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.
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عنوان ژورنال:
- CoRR
دوره abs/1710.10779 شماره
صفحات -
تاریخ انتشار 2017