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Music Signal Separation by Orthogonality and Maximum-Distance Constrained Nonnegative Matrix Factorization with Target Signal Information

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In this paper, we address the separation of multiple instrumental sources based on semi-supervised nonnegative matrix factorization (SNMF) and propose a new constrained SNMF. Recently, various types of SNMF have been proposed. In particular, we focus our attention on one type of SNMF that utilizes information on a priori bases. Indeed, this type of SNMF can achieve better separation performance. However, SNMF without are any constraint between a priori bases and other bases often degrades separation performance. Thus, we propose a new SNMF that imposes a constraint between a priori bases and other bases. An experimental result shows the efficacy of the proposed constrained SNMF.

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