Audio-Visual Speech Enhancement Using Conditional Variational Auto-Encoders

Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data. VAEs have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. One advantage of this gen...

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Vydané v:	IEEE/ACM transactions on audio, speech, and language processing Ročník 28; s. 1788 - 1800
Hlavní autori:	Sadeghi, Mostafa, Leglaive, Simon, Alameda-Pineda, Xavier, Girin, Laurent, Horaud, Radu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Predmet:	Algorithms Audio data Audio equipment Audio-visual speech enhancement Coders Computer architecture Computer Science Computer simulation Computer Vision and Pattern Recognition deep generative models Lips Machine Learning Monte Carlo expectation-maximization Noise measurement nonnegative matrix factorization Signal and Image Processing Sound Speech Speech enhancement Speech processing Training variational auto-encoders Visual signals Visualization Training Speech enhancement Visualization Lips Noise measurement Computer architecture
ISSN:	2329-9290, 2329-9304
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