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Mixtures of Local Dictionaries for Unsupervised Speech Enhancement
2015
IEEE Signal Processing Letters
We propose a novel extension of Nonnegative Matrix Factorization (NMF) that models a signal with multiple local dictionaries activated sparsely. This set of local dictionaries for a source, e.g. speech, disjointly constitute a superset that is more discriminative than an ordinary NMF dictionary, because its local structures represent the source's manifold better. A block sparsity constraint is used to regularize the NMF solutions so that only one or a small number of blocks are active at a
doi:10.1109/lsp.2014.2346506
fatcat:lwnvf3eq5jatdesfmwgud6hqlq