Multiscale blind source separation

Pavel Kisilev; Michael Zibulevsky; Yehoshua Y. Zeevi; Barak A. Pearlmutter

doi:10.1117/12.421206

26 March 2001 Multiscale blind source separation

Pavel Kisilev, Michael Zibulevsky, Yehoshua Y. Zeevi, Barak A. Pearlmutter

Proceedings Volume 4391, Wavelet Applications VIII; (2001) https://doi.org/10.1117/12.421206
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

The concern of the blind source separation problem is to extract the underlying source signals from a set of their linear mixtures, where the mixing matrix is unknown. It was discovered recently, that use of sparsity of source representation in some signal dictionary dramatically improves the quality of separation. In this work we use the property of multiscale transforms, such as wavelet or wavelet packets, to decompose signals into sets of local features with various degrees of sparsity. We use this intrinsic property for selecting the best (most sparse) subsets of features for further separation. Experiments with simulated signals, musical sounds and images demonstrate significant improvement of separation quality.

Citation Download Citation

Pavel Kisilev, Michael Zibulevsky, Yehoshua Y. Zeevi, and Barak A. Pearlmutter "Multiscale blind source separation", Proc. SPIE 4391, Wavelet Applications VIII, (26 March 2001); https://doi.org/10.1117/12.421206

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