Empirical weighting for blind source separation in a multiple-snapshots scenario

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

Abstract

We consider the blind separation of sources with general (e.g., not necessarily stationary) temporal covariance structures. When the sources' temporal covariance matrices are known, the maximum-likelihood (ML) separation scheme (for Gaussian sources) conveniently exploits this knowledge. However, in the more practical case, when these matrices are unknown, ML separation calls for their estimation from the available observations. When multiple snapshots of the mixtures are available (synchronized to some external stimulus), such estimation is possible, but might require a huge number of snapshots for attaining reasonable accuracy. Rather than estimate high-dimensional covariance matrices, we propose here a more practical ("partial"-ML) approach, based on estimation of much smaller covariance matrices. These are covariances of low-dimensional vectors, consisting of respective off-diagonal terms of spatial sample-correlation matrices. Weighted joint diagonalization of these correlation matrices (using the estimated low-dimensional covariances for the weighting) significantly improves the separation performance over alternative options, as we demonstrate in simulation.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages3704-3707
Number of pages4
DOIs
StatePublished - 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
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • BSS
  • Covariance Estimation
  • ICA

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