Single-channel speech separation based on Gaussian process regression

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Symposium on Multimedia, ISM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages275-278
Number of pages4
ISBN (Electronic)9781538668573
DOIs
StatePublished - 4 Jan 2019
Event20th IEEE International Symposium on Multimedia, ISM 2018 - Taichung, Taiwan
Duration: 10 Dec 201812 Dec 2018

Publication series

NameProceedings - 2018 IEEE International Symposium on Multimedia, ISM 2018

Conference

Conference20th IEEE International Symposium on Multimedia, ISM 2018
Country/TerritoryTaiwan
CityTaichung
Period10/12/1812/12/18

Keywords

  • Gaussian process regression
  • single-channel speech separation

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