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Monophonic sound source separation by non-negative sparse autoencoders

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/65295

Title: Monophonic sound source separation by non-negative sparse autoencoders
Authors: Zen, Keiki Browse this author
Suzuki, Masahiro Browse this author
Sato, Haruhiko Browse this author →KAKEN DB
Oyama, Satoshi Browse this author →KAKEN DB
Kurihara, Masahito Browse this author →KAKEN DB
Keywords: monophonic sound source separation
unsupervised learning
online learning
non-negative sparse autoencoder
NMF
Issue Date: 2014
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Citation: Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on, ISBN: 978-1-4799-3840-7
Start Page: 3623
End Page: 3626
Publisher DOI: 10.1109/SMC.2014.6974492
Abstract: Monophonic sound source separation is an essential subject on the fields where sound, such as voice, music and noise, is dealt with. In particular, unsupervised approaches to this problem have high versatility in comparison with supervised approaches. Non-negative matrix factorization is the most frequently used algorithm for the monophonic sound source separation without prior knowledge. This is also applied to various applications, including data clustering, face recognition, gene expression classification. However, non-negative matrix factorization cannot be efficiently used in online learning. In order to solve this difficulty, the non-negative sparse autoencoder was proposed in the literature. Although several successful applications have been reported, this is not yet applied to the monophonic sound source separation. This paper shows that the non-negative sparse autoencoder can perform the monophonic sound source separation without prior knowledge in online learning.
Conference Name: IEEE International Conference on Systems, Man and Cybernetics (SMC)
Conference Sequence: 2014
Conference Place: San Diego
Rights: © 2014 IEEE. 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.
Type: proceedings (author version)
URI: http://hdl.handle.net/2115/65295
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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