Bayesian learning for speech dereverberation

Jen-Tzung Chien, You Cheng Chang

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

This study presents a Bayesian approach to enhance the magnitude spectra of single-channel reverberant speech signals. Speech dereverberation model is constructed by using a nonnegative convolutive transfer function (NCTF) and a nonnegative matrix factorization (NMF). NCTF is used to characterize the magnitude spectra of speech signal and room impulse response while NMF is applied to represent the fine structure of speech spectra. Importantly, we deal with the variations of dereverberation model by introducing the exponential priors for reverberation kernel and noise signal. A full Bayesian solution to speech dereverberation is obtained according to the variational Bayesian inference algorithm. Using this algorithm, the room configuration and the speaker characteristics are automatically learned from data. Such a general model can be reduced to the previous methods. Experimental results on both simulated data and real recordings from 2014 REVERB Challenge show the merit of the proposed method for single-channel speech dereverberation.

原文English
主出版物標題2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
編輯Kostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
發行者IEEE Computer Society
ISBN(電子)9781509007462
DOIs
出版狀態Published - 8 十一月 2016
事件26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
持續時間: 13 九月 201616 九月 2016

出版系列

名字IEEE International Workshop on Machine Learning for Signal Processing, MLSP
2016-November
ISSN(列印)2161-0363
ISSN(電子)2161-0371

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
國家Italy
城市Vietri sul Mare, Salerno
期間13/09/1616/09/16

指紋 深入研究「Bayesian learning for speech dereverberation」主題。共同形成了獨特的指紋。

引用此