GENERATIVE ADVERSARIAL SOURCE SEPARATION

被引：0

作者：

Subakan, Y. Cem ^{[1
]}

Smaragdis, Paris ^{[1
,2
]}

机构：

[1] UIUC, Urbana, IL 61801 USA

[2] Adobe Syst, San Jose, CA USA

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2018年

基金：

美国国家科学基金会;

关键词：

Generative Adversarial Networks; Source Separation; Generative Models;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：26 / 30

页数：5

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