We consider the problem of blindly separating time-varying instantaneous mixtures. It is assumed that the arbitrary time dependency of the mixing coefficient, is known up to a finite number of parameters. Using sparse (or sparsified) sources, we geometrically identify samples of the curves representing the parametric model. The parameters are found using a probabilistic approach of estimating the maximum likelihood of a curve, given the data. After identifying the model parameters, the mixing system is inverted to estimate the sources. The new approach to blind separation of time-varying mixtures is demonstrated using both synthetic and real data. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Kaftory, R., & Zeevi, Y. Y. (2007). Probabilistic geometric approach to blind separation of time-varying mixtures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 373–380). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_47
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