Semi-Supervised Multichannel Speech Enhancement With a Deep Speech Prior

This paper describes a semi-supervised multichannel speech enhancement method that uses clean speech data for prior training. Although multichannel nonnegative matrix factorization (MNMF) and its constrained variant called independent low-rank matrix analysis (ILRMA) have successfully been used for...

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Vydáno v:	IEEE/ACM transactions on audio, speech, and language processing Ročník 27; číslo 12; s. 2197 - 2212
Hlavní autoři:	Sekiguchi, Kouhei, Bando, Yoshiaki, Nugraha, Aditya Arie, Yoshii, Kazuyoshi, Kawahara, Tatsuya
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.12.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Covariance matrix Data models deep learning Matrix methods Maximum likelihood estimation Multichannel speech enhancement Noise Noise measurement nonnegative matrix factorization Parameter sensitivity Probabilistic logic Probabilistic models Speech Speech enhancement Speech processing Time-frequency analysis variational autoencoder
ISSN:	2329-9290, 2329-9304
On-line přístup:	Získat plný text
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