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- W2045402776 abstract "In this paper we propose an approach for the problem of single channel source separation of speech and music signals. Our approach is based on representing each source's power spectral density using dictionaries and nonlinearly projecting the mixture signal spectrum onto the combined span of the dictionary entries. We encourage sparsity and continuity of the dictionary coefficients using penalty terms (or log-priors) in an optimization framework. We propose to use a novel coordinate descent technique for optimization, which nicely handles nonnegativity constraints and nonquadratic penalty terms. We use an adaptive Wiener filter, and spectral subtraction to reconstruct both of the sources from the mixture data after corresponding power spectral densities (PSDs) are estimated for each source. Using conventional metrics, we measure the performance of the system on simulated mixtures of single person speech and piano music sources. The results indicate that the proposed method is a promising technique for low speech-to-music ratio conditions and that sparsity and continuity priors help improve the performance of the proposed system." @default.
- W2045402776 created "2016-06-24" @default.
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- W2045402776 date "2010-08-01" @default.
- W2045402776 modified "2023-10-04" @default.
- W2045402776 title "Semi-blind Speech-Music Separation Using Sparsity and Continuity Priors" @default.
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- W2045402776 doi "https://doi.org/10.1109/icpr.2010.1129" @default.
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