Online Gaussian process for nonstationary speech separation

Hsin Lung Hsieh*, Jen-Tzung Chien

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

In a practical speech enhancement system, it is required to enhance speech signals from the mixed signals, which were corrupted due to the nonstationary source signals and mixing conditions. The source voices may be from different moving speakers. The speakers may abruptly appear or disappear and may be permuted continuously. To deal with these scenarios with a varying number of sources, we present a new method for nonstationary speech separation. An online Gaussian process independent component analysis (OLGP-ICA) is developed to characterize the real-time temporal structure in time-varying mixing system and to capture the evolved statistics of independent sources from online observed signals. A variational Bayes algorithm is established to estimate the evolved parameters for dynamic source separation. In the experiments, the proposed OLGP-ICA is compared with other ICA methods and is illustrated to be effective in recovering speech and music signals in a nonstationary speaking environment.

Original languageEnglish
Pages394-397
Number of pages4
StatePublished - 1 Dec 2010
Event11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan
Duration: 26 Sep 201030 Sep 2010

Conference

Conference11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010
CountryJapan
CityMakuhari, Chiba
Period26/09/1030/09/10

Keywords

  • Gaussian process
  • Online learning
  • Speech enhancement
  • Speech separation
  • Variational bayes

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