A review on speech separation using NMF and its extensions

Tuan Pham, Yuan Shan Lee, Yu An Chen, Jia Ching Wang

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

1 Scopus citations

Abstract

Speech separation aims to estimate the target signals produced by individual speech sources from a mixture signal. In this paper, we especially review on data-driven separation methods, where algorithms will be enhanced to produce better dictionary learning which considers the geometric of input data and efficiently performs separation mixture. We review the existing algorithms using non-negative matrix factorization, sparse coding, mixture local dictionary, group lasso, and graph regularization to produce knowledge bases. We also review the extension of NMF by incorporating two state-of-art techniques i.e. bilevel optimization and deep neural network.

Original languageEnglish
Title of host publicationProceedings of 2015 International Conference on Orange Technologies, ICOT 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages26-29
Number of pages4
ISBN (Electronic)9781467382373
DOIs
StatePublished - 22 Jun 2016
Event3rd International Conference on Orange Technologies, ICOT 2015 - Hong Kong, Hong Kong
Duration: 19 Dec 201522 Dec 2015

Publication series

NameProceedings of 2015 International Conference on Orange Technologies, ICOT 2015

Conference

Conference3rd International Conference on Orange Technologies, ICOT 2015
Country/TerritoryHong Kong
CityHong Kong
Period19/12/1522/12/15

Keywords

  • bilevel optimization
  • graph regularization
  • group lasso
  • non-negative matrix factorization
  • single channel source separation

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