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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/16658

Title: Monaural Singing Voice Separation Using Fusion-Net with Time-Frequency Masking
Authors: Li, Feng
Qian, Kaizhi
Hasegawa-Johnson, Mark
Akagi, Masato
Issue Date: 2019-11-20
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Start page: 1239
End page: 1243
DOI: 10.1109/APSIPAASC47483.2019.9023055
Abstract: Monaural singing voice separation has received much attention in recent years. In this paper, we propose a novel neural network architecture for monaural singing voice separation, Fusion-Net, which is combining U-Net with the residual convolutional neural network to develop a much deeper neural network architecture with summation-based skip connections. In addition, we apply time-frequency masking to improve the separation results. Finally, we integrate the phase spectra with magnitude spectra as the post-processing to optimize the separated singing voice from the mixture music. Experimental results demonstrate that the proposed method can achieve better separation performance than the previous U-Net architecture on the ccMixter database.
Rights: This is the author's version of the work. Copyright (C) 2019 IEEE. 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2019, pp.1239-1243. 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/10119/16658
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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