Blind Source Separation via Multinode Sparse Representation

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Michael Zibulevsky, Pavel Kisilev, Yehoshua Zeevi, Barak Pearlmutter


We consider a problem of blind source separation from a set of instan(cid:173) taneous linear mixtures, where the mixing matrix is unknown. It was discovered recently, that exploiting the sparsity of sources in an appro(cid:173) priate representation according to some signal dictionary, dramatically improves the quality of separation. In this work we use the property of multi scale 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. The performance of the algorithm is ver(cid:173) ified on noise-free and noisy data. Experiments with simulated signals, musical sounds and images demonstrate significant improvement of sep(cid:173) aration quality over previously reported results.