Abstract
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.
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Kaftory, R., Zeevi, Y.Y. (2007). Probabilistic Geometric Approach to Blind Separation of Time-Varying Mixtures. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_47
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DOI: https://doi.org/10.1007/978-3-540-74494-8_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
Online ISBN: 978-3-540-74494-8
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