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 |
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. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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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) |
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Keyword(1) |
Voice activity detection |
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high-level noise environments |
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deep learning |
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convolutional neural networks |
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smoothing |
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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 |
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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 |
#Pages |
6 |
Date of Issue |
2019-03-07 (EA, SIP, SP) |