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Paper Abstract and Keywords
Presentation 2019-03-15 13:30
[Poster Presentation] Design and Evaluation of Ladder Denoising Autoencoder for Auditory Speech Feature Extraction of Overlapped Speech Separation
Hiroshi Sekiguchi, Yoshiaki Narusue, Hiroyuki Morikawa (Univ. of Tokyo) EA2018-155 SIP2018-161 SP2018-117
Abstract (in Japanese) (See Japanese page) 
(in English) Primates and mammalian distinguish overlapped speech sounds from one another by recognizing a single sound source whether temporal contour of auditory speech features in auditory neurons are similar.This is called as temporal synchronization detection and clustering function. The performance of this detection and clustering function depends on the former function that is auditory speech feature extraction. The feature extraction is needed to satisfy two requirements: 1) auditory speech features are mutually stochastic independent and 2) the feature extraction should allow various complicated sounds to be represented, which leads to non-linearity.This paper introduces non-linear sparse encoder decoder model as computational model, implements it with a ladder network, and finally evaluate that network behavior. We show that our trained network can output auditory speech features that are mutually stochastic independent.
Keyword (in Japanese) (See Japanese page) 
(in English) Ladder Network / Speech Separation / Auditory Neuroscience / Auditory Speech Feature Extraction / stochastic independence / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 497, SP2018-117, pp. 329-333, March 2019.
Paper # SP2018-117 
Date of Issue 2019-03-07 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF EA2018-155 SIP2018-161 SP2018-117

Conference Information
Committee EA SIP SP  
Conference Date 2019-03-14 - 2019-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) i+Land nagasaki (Nagasaki-shi) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Engineering/Electro Acoustics, Signal Processing, Speech, and Related Topics 
Paper Information
Registration To SP 
Conference Code 2019-03-EA-SIP-SP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Design and Evaluation of Ladder Denoising Autoencoder for Auditory Speech Feature Extraction of Overlapped Speech Separation 
Sub Title (in English)  
Keyword(1) Ladder Network  
Keyword(2) Speech Separation  
Keyword(3) Auditory Neuroscience  
Keyword(4) Auditory Speech Feature Extraction  
Keyword(5) stochastic independence  
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1st Author's Name Hiroshi Sekiguchi  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Yoshiaki Narusue  
2nd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
3rd Author's Name Hiroyuki Morikawa  
3rd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
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Speaker
Date Time 2019-03-15 13:30:00 
Presentation Time 90 
Registration for SP 
Paper # IEICE-EA2018-155,IEICE-SIP2018-161,IEICE-SP2018-117 
Volume (vol) IEICE-118 
Number (no) no.495(EA), no.496(SIP), no.497(SP) 
Page pp.329-333 
#Pages IEICE-5 
Date of Issue IEICE-EA-2019-03-07,IEICE-SIP-2019-03-07,IEICE-SP-2019-03-07 


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