Contrast functions for blind source separation based on time-frequency information-theory

Authors:
Mohamed Sahmoudi;Moeness G. Amin;K. Abed-Meraim;A. Belouchrani
Affiliations:
Center for Advanced Communications, Villanova University, Villanova, PA;Center for Advanced Communications, Villanova University, Villanova, PA;TSI, Telecom Paris, Paris;EE Dept., Ecole Polytechnique, Algiers, Algeria
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 4
Cited 1

Blind separation of sources based on their time-frequency signatures

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Information–Theoretic nonstationary source separation

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Measuring time-frequency information content using the Renyi entropies

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Abstract

This paper introduces new contrast functions for blind separation of sources with different time-frequency signatures. Two contrast functions based on the Kullback-Leibler and Jensen-Rényi divergences in the time-frequency (T-F) plane are introduced. Two iterative algorithms are proposed for the proposed contrasts optimization and source separation. One algorithm consists of spatial whitening and gradient-Jacobi maximization, combining Givens rotations and stochastic gradient. The second algorithm uses a quasi-Newton technique.