Improved Wasserstein conditional generative adversarial network speech enhancement

Improved Wasserstein conditional generative adversarial network speech enhancement

Abstract The speech enhancement based on the generative adversarial network has achieved excellent results with large quantities of data, but performance in the low-data regime and tasks like unseen data learning still lag behind. In this work, we model Wasserstein Conditional Generative Adversarial...

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Bibliographic Details
Main Authors: Shan Qin, Ting Jiang
Format: Article
Language:English
Published: SpringerOpen 2018-07-01
Series:EURASIP Journal on Wireless Communications and Networking
Subjects:
Wasserstein conditional GAN
Speech enhancement
Generalization
Online Access:http://link.springer.com/article/10.1186/s13638-018-1196-0

Internet

http://link.springer.com/article/10.1186/s13638-018-1196-0

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