Temporal attention based animal sound classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jungmin | - |
dc.contributor.author | Lee, Younglo | - |
dc.contributor.author | Kim, Donghyeon | - |
dc.contributor.author | Ko, Hanseok | - |
dc.date.accessioned | 2021-08-31T16:09:35Z | - |
dc.date.available | 2021-08-31T16:09:35Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 1225-4428 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/59024 | - |
dc.description.abstract | In this paper, to improve the classification accuracy of bird and amphibian acoustic sound, we utilize GLU (Gated Linear Unit) and Self-attention that encourages the network to extract important features from data and discriminate relevant important frames from all the input sequences for further performance improvement. To utilize acoustic data, we convert 1-D acoustic data to a log-Mel spectrogram. Subsequently, undesirable component such as background noise in the log-Mel spectrogram is reduced by GLU. Then, we employ the proposed temporal self-attention to improve classification accuracy. The data consist of 6-species of birds, 8-species of amphibians including endangered species in the natural environment. As a result, our proposed method is shown to achieve an accuracy of 91 % with bird data and 93 % with amphibian data. Overall, an improvement of about 6 % similar to 7 % accuracy in performance is achieved compared to the existing algorithms. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | ACOUSTICAL SOC KOREA | - |
dc.title | Temporal attention based animal sound classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ko, Hanseok | - |
dc.identifier.doi | 10.7776/ASK.2020.39.5.406 | - |
dc.identifier.wosid | 000594710300004 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA, v.39, no.5, pp.406 - 413 | - |
dc.relation.isPartOf | JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA | - |
dc.citation.title | JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA | - |
dc.citation.volume | 39 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 406 | - |
dc.citation.endPage | 413 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002628467 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Acoustics | - |
dc.relation.journalWebOfScienceCategory | Acoustics | - |
dc.subject.keywordAuthor | Audio event classification | - |
dc.subject.keywordAuthor | Convolution Neural Network (CNN) | - |
dc.subject.keywordAuthor | Self-attention | - |
dc.subject.keywordAuthor | Gated Linear Unit (GLU) | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.