Paper Abstract and Keywords |
Presentation |
2022-03-02 15:35
[Poster Presentation]
A study of shout detection for clipped speech Taito Ishida, Kazuhiro Matsuda, Takahiro Fukumori, Yoichi Yamashita (Ritsumeikan Univ.) EA2021-97 SIP2021-124 SP2021-82 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recently, several audio surveillance systems using shouted speech have been proposed for safety in daily life.
Although only clean speech is used for training in conventional shout detection methods based on deep learning, noise in noisy environment and clipping distort target speech in real-world situations.
This paper proposes a shout detection method based on deep learning with multi-condition learning for clipped speech.
We compare the following three types of multi-condition learning: using only unclipped speech, using unclipped and clipped speech, and using unclipped and declipped speech.
In this study, we evaluate the detection performance of clipped speech in various noisy environments.
The results of experiments conducted in various noisy environments and clipping conditions demonstrate that proposed methods using multi-condition learning both better detection performance than the conventional method using only clean speech.
In addition,results of experiments show that the proposed method can detect shouted speech with clipping ratios that is not included in training dataset. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
shout / deep learning / clipped speech / declipping / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 385, SP2021-82, pp. 207-212, March 2022. |
Paper # |
SP2021-82 |
Date of Issue |
2022-02-22 (EA, SIP, SP) |
ISSN |
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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EA2021-97 SIP2021-124 SP2021-82 |
Conference Information |
Committee |
EA SIP SP IPSJ-SLP |
Conference Date |
2022-03-01 - 2022-03-02 |
Place (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
SP |
Conference Code |
2022-03-EA-SIP-SP-SLP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A study of shout detection for clipped speech |
Sub Title (in English) |
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Keyword(1) |
shout |
Keyword(2) |
deep learning |
Keyword(3) |
clipped speech |
Keyword(4) |
declipping |
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1st Author's Name |
Taito Ishida |
1st Author's Affiliation |
Ritsumeikan University (Ritsumeikan Univ.) |
2nd Author's Name |
Kazuhiro Matsuda |
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 |
Yoichi Yamashita |
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Ritsumeikan University (Ritsumeikan Univ.) |
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Speaker |
Author-1 |
Date Time |
2022-03-02 15:35:00 |
Presentation Time |
120 minutes |
Registration for |
SP |
Paper # |
EA2021-97, SIP2021-124, SP2021-82 |
Volume (vol) |
vol.121 |
Number (no) |
no.383(EA), no.384(SIP), no.385(SP) |
Page |
pp.207-212 |
#Pages |
6 |
Date of Issue |
2022-02-22 (EA, SIP, SP) |
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