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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/15777

Title: Unsupervised Singing Voice Separation Using Gammatone Auditory Filterbank and Constraint Robust Principal Component Analysis
Authors: Li, Feng
Akagi, Masato
Issue Date: 2018-11-15
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Start page: 1924
End page: 1928
DOI: 10.23919/APSIPA.2018.8659640
Abstract: This paper presents an unsupervised singing voice separation algorithm which using an extension of robust principal component analysis (RPCA) with rank-1 constraint (CRPCA) based on gammatone auditory filterbank on cochleagram. Unlike the conventional algorithms that focus on spectrogram analysis or its variants, we develop an extension of RPCA on cochleagram using an alternative time-frequency representation based on gammatone auditory filterbank. We also apply time-frequency masking to improve the results of separated low-rank and sparse matrices by using CRPCA method. Evaluation results demonstrate that the proposed algorithm can achieve better separation performance on MIR-1K dataset.
Rights: This is the author's version of the work. Copyright (C) 2018 IEEE. 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2018, 1924-1928. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/15777
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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