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Paper Abstract and Keywords
Presentation 2018-03-20 09:00
[Poster Presentation] Performance evaluation of unknown sound clustering for indoor-environmental sound classification based on self-generated acoustic model
Sakiko Mishima, Yukoh Wakabayashi, Takahiro Fukumori, Keisuke Imoto, Masato Nakayama, Takanobu Nishiura (Ritsumeikan Univ.) EA2017-152 SIP2017-161 SP2017-135
Abstract (in Japanese) (See Japanese page) 
(in English) Indoor-environmental sound classification is useful for surveillance systems which monitor the situations in the dark and blind areas. In previous research, the acoustic model has been constructed to classify the sounds. However, this approach incorrectly classifies unknown-class sound into known-class one. We then focus on a classification method with an self-generated acoustic model to prevent such misclassification. The model is constructed using the sounds which are classified into unknown-class by known/unknown sound classification method.
In this paper, we propose a clustering method using output probability distributions of deep neural network. To confirm the performance of unknown sound clustering, we carried out the evaluation experiment.
Keyword (in Japanese) (See Japanese page) 
(in English) Environmental sound classification / Self-generated acoustic model / Deep neural network / Clustering / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 516, SIP2017-161, pp. 277-280, March 2018.
Paper # SIP2017-161 
Date of Issue 2018-03-12 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
and
reproduction
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. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF EA2017-152 SIP2017-161 SP2017-135

Conference Information
Committee SIP EA SP MI  
Conference Date 2018-03-19 - 2018-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics [SIP, EA, SP]/ Medical Image Engineering, Analysis, Recognition, etc. [MI] 
Paper Information
Registration To SIP 
Conference Code 2018-03-SIP-EA-SP-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Performance evaluation of unknown sound clustering for indoor-environmental sound classification based on self-generated acoustic model 
Sub Title (in English)  
Keyword(1) Environmental sound classification  
Keyword(2) Self-generated acoustic model  
Keyword(3) Deep neural network  
Keyword(4) Clustering  
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1st Author's Name Sakiko Mishima  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Yukoh Wakabayashi  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
3rd Author's Name Takahiro Fukumori  
3rd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
4th Author's Name Keisuke Imoto  
4th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
5th Author's Name Masato Nakayama  
5th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
6th Author's Name Takanobu Nishiura  
6th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker
Date Time 2018-03-20 09:00:00 
Presentation Time 90 
Registration for SIP 
Paper # IEICE-EA2017-152,IEICE-SIP2017-161,IEICE-SP2017-135 
Volume (vol) IEICE-117 
Number (no) no.515(EA), no.516(SIP), no.517(SP) 
Page pp.277-280 
#Pages IEICE-4 
Date of Issue IEICE-EA-2018-03-12,IEICE-SIP-2018-03-12,IEICE-SP-2018-03-12 


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