Automatic Detection of Cow's Oestrus in Audio Surveillance System
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chung, Y. | - |
dc.contributor.author | Lee, J. | - |
dc.contributor.author | Oh, S. | - |
dc.contributor.author | Park, D. | - |
dc.contributor.author | Chang, H. H. | - |
dc.contributor.author | Kim, S. | - |
dc.date.accessioned | 2021-09-06T00:15:27Z | - |
dc.date.available | 2021-09-06T00:15:27Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2013-07 | - |
dc.identifier.issn | 1011-2367 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/102848 | - |
dc.description.abstract | Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ASIAN-AUSTRALASIAN ASSOC ANIMAL PRODUCTION SOC | - |
dc.subject | DAIRY-COWS | - |
dc.subject | CATTLE | - |
dc.subject | TIME | - |
dc.subject | AGRICULTURE | - |
dc.subject | RECOGNITION | - |
dc.subject | PEDOMETER | - |
dc.subject | FOOD | - |
dc.title | Automatic Detection of Cow's Oestrus in Audio Surveillance System | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chung, Y. | - |
dc.contributor.affiliatedAuthor | Park, D. | - |
dc.identifier.doi | 10.5713/ajas.2012.12628 | - |
dc.identifier.scopusid | 2-s2.0-84880292439 | - |
dc.identifier.wosid | 000321419800017 | - |
dc.identifier.bibliographicCitation | ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES, v.26, no.7, pp.1030 - 1037 | - |
dc.relation.isPartOf | ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES | - |
dc.citation.title | ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES | - |
dc.citation.volume | 26 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 1030 | - |
dc.citation.endPage | 1037 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART001781348 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.description.journalRegisteredClass | other | - |
dc.relation.journalResearchArea | Agriculture | - |
dc.relation.journalWebOfScienceCategory | Agriculture, Dairy & Animal Science | - |
dc.subject.keywordPlus | DAIRY-COWS | - |
dc.subject.keywordPlus | CATTLE | - |
dc.subject.keywordPlus | TIME | - |
dc.subject.keywordPlus | AGRICULTURE | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | PEDOMETER | - |
dc.subject.keywordPlus | FOOD | - |
dc.subject.keywordAuthor | Cow&apos | - |
dc.subject.keywordAuthor | s Oestrus Detection | - |
dc.subject.keywordAuthor | Sound Data | - |
dc.subject.keywordAuthor | Mel Frequency Cepstrum Coefficient | - |
dc.subject.keywordAuthor | Feature Subset Selection | - |
dc.subject.keywordAuthor | Support Vector Data Description | - |
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.