Separating More Sources than Sensors using Time-Frequency Distributions

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Separating More Sources than Sensors using Time-Frequency Distributions

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dc.contributor.author Nguyen, L-T
dc.contributor.author Belouchrani, A
dc.contributor.author Abed-Meraim, K
dc.contributor.author Boashash, B
dc.date.accessioned 2012-06-18T17:07:51Z
dc.date.available 2012-06-18T17:07:51Z
dc.date.issued 2001-08
dc.identifier.citation L-T. Nguyen, A. Belouchrani, K. Abed-Meraim, and B. Boashash, “Separating More Sources than Sensors using Time-Frequency Distributions”, 6th International Symposium on Signal Processing and its Applications, ISSPA 2001, Kuala Lumpur, Malaysia, Vol. II, pp 583-586, August, 2001. en_US
dc.identifier.uri http://dx.doi.org/10.1109/isspa.2001.950212
dc.identifier.uri http://hdl.handle.net/10576/10858
dc.description.abstract This paper deals with the problem of blind source separation of nonstationary signals of which only instantaneous linear signals are observed. Exploiting the effectiveness of time-frequency signal processing for nonstationary signals, a blind source separation approach is considered using the observation spatial time-frequency distributions (STFD). Existing solutions are bound to the situation in which the number of sources being separated is less than the number of available sensors measuring the mixed sources. We consider the more general case when we can have more sources than sensors assuming that the former are "separable" in the time-frequency domain. The proposed solution proceeds through 3 main steps: (i) a testing procedure is applied (after whitening the STFD) to first separate the cross-terms from auto-terms; (ii), source separation in the time-frequency domain (from the autoterms only) using a vector classification approach; and finally (iii), obtaining the source signatures using time-frequency synthesis. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.title Separating More Sources than Sensors using Time-Frequency Distributions en_US
dc.type Article en_US

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