Vocalization Data Mining For Estimating Swine Stress Conditions

dc.creatorMoi M.
dc.creatorNaas I.A.
dc.creatorCaldara F.R.
dc.creatorPaz I.C.L.A.
dc.creatorGarcia R.G.
dc.creatorCordeiro A.F.S.
dc.date2014
dc.date2015-06-25T18:01:31Z
dc.date2015-11-26T15:03:18Z
dc.date2015-06-25T18:01:31Z
dc.date2015-11-26T15:03:18Z
dc.date.accessioned2018-03-28T22:14:10Z
dc.date.available2018-03-28T22:14:10Z
dc.identifier
dc.identifierEngenharia Agricola. Sociedade Brasileira De Engenharia Agricola, v. 34, n. 3, p. 445 - 450, 2014.
dc.identifier1006916
dc.identifier10.1590/S0100-69162014000300008
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84906250220&partnerID=40&md5=2b10a8ec004bea6274e03b145f3eeb7d
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/87603
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/87603
dc.identifier2-s2.0-84906250220
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1256581
dc.descriptionThis study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1.
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dc.description445
dc.description450
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dc.languageen
dc.publisherSociedade Brasileira de Engenharia Agricola
dc.relationEngenharia Agricola
dc.rightsaberto
dc.sourceScopus
dc.titleVocalization Data Mining For Estimating Swine Stress Conditions
dc.typeArtículos de revistas


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