Single-channel speech separation based on Gaussian process regression

Le Dinh Nguyen, Sih Huei Chen, Tzu Chiang Tai, Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Gaussian process (GP) is a flexible kernel-based learning method which has found widespread applications in signal processing. In this paper, a supervised approach is proposed to handle single-channel speech separation (SCSS) problem. We focus on modeling a nonlinear mapping between mixed and clean speeches based on GP regression, in which reconstructed audio signal is estimated by the predictive mean of GP model. The nonlinear conjugate gradient method was utilized to perform the hyper-parameter optimization. The experiment on a subset of TIMIT speech dataset is carried out to confirm the validity of the proposed approach.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2018 IEEE International Symposium on Multimedia, ISM 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面275-278
頁數4
ISBN(電子)9781538668573
DOIs
出版狀態已出版 - 2 7月 2018
事件20th IEEE International Symposium on Multimedia, ISM 2018 - Taichung, Taiwan
持續時間: 10 12月 201812 12月 2018

出版系列

名字Proceedings - 2018 IEEE International Symposium on Multimedia, ISM 2018

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???event.eventtypes.event.conference???20th IEEE International Symposium on Multimedia, ISM 2018
國家/地區Taiwan
城市Taichung
期間10/12/1812/12/18

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