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Paper Abstract and Keywords
Presentation 2019-03-14 13:30
[Poster Presentation] Voice activity detection under high levels of noise using gated convolutional neural networks
Li Li, Koshino Yuki, Matsumoto Mitsuo, Makino Shoji (Univ. Tsukuba) EA2018-102 SIP2018-108 SP2018-64
Abstract (in Japanese) (See Japanese page) 
(in English) This paper deals with voice activity detection (VAD) tasks under high-level noise environments where signal-to-noise ratios (SNRs) are lower than -5 dB. Many VAD approaches have been developed during recent decades and shown to be efficient and effective. However, these approaches tend to fail the detection when SNRs become critically low in real situations, such as rescue robots in a disaster or navigation in a high-speed moving car. On the other hand, the deep learning techniques have achieved state-of-art results in many difficult classification tasks and shown the high potential to be able to solve the difficult VAD tasks. To achieve accurate VAD results under high-level noise environments, this paper proposes a gated convolutional neural network-based approach that is able to capture long- and short-term dependencies in time series as cues for detection. Experimental evaluations using high-level ego-noise of a hose-shaped rescue robot revealed that the proposed method was able to averagely achieve accurate VAD results in environments with SNR in the range of -30 dB to -5 dB.
Keyword (in Japanese) (See Japanese page) 
(in English) Voice activity detection / high-level noise environments / deep learning / convolutional neural networks / smoothing / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 495, EA2018-102, pp. 19-24, March 2019.
Paper # EA2018-102 
Date of Issue 2019-03-07 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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 EA2018-102 SIP2018-108 SP2018-64

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 EA 
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) Voice activity detection under high levels of noise using gated convolutional neural networks 
Sub Title (in English)  
Keyword(1) Voice activity detection  
Keyword(2) high-level noise environments  
Keyword(3) deep learning  
Keyword(4) convolutional neural networks  
Keyword(5) smoothing  
1st Author's Name Li Li  
1st Author's Affiliation University of Tsukuba (Univ. Tsukuba)
2nd Author's Name Koshino Yuki  
2nd Author's Affiliation University of Tsukuba (Univ. Tsukuba)
3rd Author's Name Matsumoto Mitsuo  
3rd Author's Affiliation University of Tsukuba (Univ. Tsukuba)
4th Author's Name Makino Shoji  
4th Author's Affiliation University of Tsukuba (Univ. Tsukuba)
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Speaker Author-1 
Date Time 2019-03-14 13:30:00 
Presentation Time 90 minutes 
Registration for EA 
Paper # EA2018-102, SIP2018-108, SP2018-64 
Volume (vol) vol.118 
Number (no) no.495(EA), no.496(SIP), no.497(SP) 
Page pp.19-24 
Date of Issue 2019-03-07 (EA, SIP, SP) 

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