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title:(Sparse AND Non-negative AND Matrix AND Factor AND 2-D AND Deconvolution)
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Sparse Non-negative Matrix Factor 2-D Deconvolution
and prove the convergence of two algorithms for NMF2D based on minimizing the squared error and the Kullback-Leibler divergence respectively. Next, we introduce a sparse non-negative matrix factor 2-D deconvolution model that gives easy interpretable decompositions and devise two algorithms for computing
Year: 2006
Language: English
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Report
Sparse Non-negative Matrix Factor 2-D Deconvolution for Automatic Transcription of Polyphonic Music
We present a novel method for automatic transcription of polyphonic music based on a recently published algorithm for non-negative matrix factor 2-D deconvolution. The method works by simultaneously estimating a time-frequency model for an instrument and a pattern corresponding to the notes which
Year: 2006
Language: English
