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- W1529295964 abstract "Measures of sparsity are useful in many aspects of audio signal processing including speech enhancement, audio coding and singing voice enhancement, and the well-known method for these applications is non-negative matrix factorization (NMF), which decomposes a non-negative data matrix into two non-negative matrices. Although previous studies on NMF have focused on the sparsity of the two matrices, the sparsity of reconstruction errors between a data matrix and the two matrices is also important, since designing the sparsity is equivalent to assuming the nature of the errors. We propose a new NMF technique, which we called L <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</inf> -norm NMF, that minimizes the L <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</inf> norm of the reconstruction errors, and derive a computationally efficient algorithm for L <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</inf> -norm NMF according to an auxiliary function principle. This algorithm can be generalized for the factorization of a real-valued matrix into the product of two real-valued matrices. We apply the algorithm to singing voice enhancement and show that adequately selecting p improves the enhancement." @default.
- W1529295964 created "2016-06-24" @default.
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- W1529295964 date "2015-04-01" @default.
- W1529295964 modified "2023-10-01" @default.
- W1529295964 title "Lp-norm non-negative matrix factorization and its application to singing voice enhancement" @default.
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- W1529295964 doi "https://doi.org/10.1109/icassp.2015.7178344" @default.
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