Independent component analysis of time/position varying mixtures

Authors:
Michael Shamis;Yehoshua Y. Zeevi
Affiliations:
Department of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel;Department of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

Citing 2
Cited 0

Blind Source Separation by Sparse Decomposition in a Signal Dictionary

Neural Computation
Probabilistic geometric approach to blind separation of time-varying mixtures

ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation

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Abstract

Blind Source Separation (BSS) is a well known problem that has been addressed in numerous studies in the last few decades. Most of the studies in this field address the problem of time/position invariant mixtures of multiple sources. Real problems are however usually not time and/or position invariant, and much more complicated. We present an extension of the Maximum Likelihood (ML) Independent Component Analysis (ICA) approach to time variant instantaneous mixtures.