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Abstract:
We consider a problem of blind source separation from a set of
instantaneous linear mixtures, where the mixing matrix is
unknown. It was discovered recently, that exploiting the sparsity
of sources in an appopriate representation according to 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
singals 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. The
performance of the algorithm is verified on noise-free and noisy
data. Experiments with simulated signals, musical sounds and
images demonstrate significant improvement of separation quality
over previously reported results.
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