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| Presentation | 2020-11-20 09:00 [Poster Presentation] Sound detection for laughter by using features based on auditory attributes Soichiro Tanaka (JAIST), Shota Morita (Fukuyama Univ), Masashi Unoki (JAIST)  EA2020-24 EMM2020-39 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper proposes a laughter detection method based on auditory attributes to detect special laughter such as a fake laughter. The proposed method consists of feature extraction and identification sections used three features: acoustic features, sound quality metrics (SQM), and timbral attributes (TA) as auditory impression. Threshold determination and support vector machine (SVM) were used in the identification section. Laughter and voices (as non-laughter) were analyzed to determine thresholds for identifying laughter on SQM and TA, then these results of thresholding were trained by using the SVM for laughter detection. The proposed methods were evaluated using both sounds of laughter and non-laughter. As results, the detection rate of the base proposed method could be improved about 20% and the false acceptance rate was halved compared with the conventional method that used only acoustic features. Furthermore, the detection rate of fake laughter and nose laughter could be improved by the proposed method in which features were selectively chosen. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Laughter Detection / Acoustic Features / Sound Quality Metrics / Timbral Attributes / Machine Learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 241, EA2020-24, pp. 15-20, Nov. 2020. | |
| Paper # | EA2020-24 | |
| Date of Issue | 2020-11-13 (EA, EMM) | |
| 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) | |
| Download PDF | EA2020-24 EMM2020-39 |
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| Conference Information | |
| Committee | EA ASJ-H EMM |
| Conference Date | 2020-11-20 - 2020-11-20 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | [Beginners Session] Engineering/Electro Acoustics, Content Processing, Digital Watermarking, Psychological and Physiological Acoustics, and Related Topics |
| Paper Information | |
| Registration To | EA |
| Conference Code | 2020-11-EA-H-EMM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Sound detection for laughter by using features based on auditory attributes |
| Sub Title (in English) | |
| Keyword(1) | Laughter Detection |
| Keyword(2) | Acoustic Features |
| Keyword(3) | Sound Quality Metrics |
| Keyword(4) | Timbral Attributes |
| Keyword(5) | Machine Learning |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Soichiro Tanaka |
| 1st Author's Affiliation | Japan Advanced Institute of Science and Technology (JAIST) |
| 2nd Author's Name | Shota Morita |
| 2nd Author's Affiliation | Fukuyama University (Fukuyama Univ) |
| 3rd Author's Name | Masashi Unoki |
| 3rd Author's Affiliation | Japan Advanced Institute of Science and Technology (JAIST) |
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| Speaker | Author-1 |
| Date Time | 2020-11-20 09:00:00 |
| Presentation Time | 120 minutes |
| Registration for | EA |
| Paper # | EA2020-24, EMM2020-39 |
| Volume (vol) | vol.120 |
| Number (no) | no.241(EA), no.242(EMM) |
| Page | pp.15-20 |
| #Pages | 6 |
| Date of Issue | 2020-11-13 (EA, EMM) |
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