Nonstationary and temporally correlated source separation using Gaussian process

Hsin Lung Hsieh*, Jen-Tzung Chien

*Corresponding author for this work

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

3 Scopus citations

Abstract

Blind source separation (BSS) is a process to reconstruct source signals from the mixed signals. The standard BSS methods assume a fixed set of stationary source signals with the fixed distribution functions. However, in practical mixing systems, the source signals are nonstationary and temporally correlated; e.g. source signal may be abruptly active or inactive or even replaced by a new one. The mixing system is also time-varying. In this paper, we present a novel Gaussian process (GP) to characterize the time-varying mixing coefficients and the temporally correlated source signals. An online variational Bayesian algorithm is established to learn the noisy mixing process where GP priors are adopted to express the correlated sources as well as the mixing matrix. Experimental results demonstrate the effectiveness of proposed method in speech separation under different scenarios.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2120-2123
Number of pages4
DOIs
StatePublished - 18 Aug 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • Bayesian method
  • Blind source separation
  • Gaussian process
  • online learning

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