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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18018

Title: Combining F0 and non-negative constraint robust principal component analysis for singing voice separation
Authors: Li, Feng
Akagi, Masato
Keywords: Singing voice separation
Robust principal component analysis
Non-negative rank-1 constraint
F0
Issue Date: 2019-12-14
Publisher: Elsevier
Magazine name: Signal Processing
Volume: 170
Start page: 107432
DOI: 10.1016/j.sigpro.2019.107432
Abstract: Separating singing voice from a musical mixture remains an important task in the field of music information retrieval. Recent studies on singing voice separation have shown that robust principal component analysis (RPCA) with rank-1 constraint approach can improve separation quality. However, the performance of separation is limited because the vocal part can not be described well by the separated matrix. Therefore, prior information such as fundamental frequency (F0) should be considered. F0 can significantly improve separation performance by removing the spectral components of non-repeating instruments (e.g., bass and guitar). In this paper, we propose a novel singing voice separation algorithm by combining prior information and non-negative constraint RPCA, which incorporates F0 and non-negative rank-1 constraint minimization of singular values in RPCA instead of minimizing the nuclear norm. In addition, we use the original phase recovery in estimating the spectral components of the separated singing voice. Experimental results on the iKala and MIR-1K datasets show higher efficiency of the proposed algorithm compared with state-of-the-art methods in terms of separation accuracy.
Rights: Copyright (C)2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license (CC BY-NC-ND 4.0). [http://creativecommons.org/licenses/by-nc-nd/4.0/] NOTICE: This is the author’s version of a work accepted for publication by Elsevier. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Feng Li and Masato Akagi, Signal Processing, 170, 2019, 107432, http://dx.doi.org/10.1016/j.sigpro.2019.107432
URI: http://hdl.handle.net/10119/18018
Material Type: author
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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