Vocalization data mining for estimating swine stress conditions
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Engenharia Agrícola |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162014000300008 |
Resumo: | This 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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Vocalization data mining for estimating swine stress conditionssound intensitysound attributesanimal welfareThis 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.Associação Brasileira de Engenharia Agrícola2014-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162014000300008Engenharia Agrícola v.34 n.3 2014reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/S0100-69162014000300008info:eu-repo/semantics/openAccessMoi,MartaNääs,Irenilza de A.Caldara,Fabiana R.Paz,Ibiara C. de L. AlmeidaGarcia,Rodrigo G.Cordeiro,Alexandra F. S.eng2014-08-05T00:00:00Zoai:scielo:S0100-69162014000300008Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2014-08-05T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false |
dc.title.none.fl_str_mv |
Vocalization data mining for estimating swine stress conditions |
title |
Vocalization data mining for estimating swine stress conditions |
spellingShingle |
Vocalization data mining for estimating swine stress conditions Moi,Marta sound intensity sound attributes animal welfare |
title_short |
Vocalization data mining for estimating swine stress conditions |
title_full |
Vocalization data mining for estimating swine stress conditions |
title_fullStr |
Vocalization data mining for estimating swine stress conditions |
title_full_unstemmed |
Vocalization data mining for estimating swine stress conditions |
title_sort |
Vocalization data mining for estimating swine stress conditions |
author |
Moi,Marta |
author_facet |
Moi,Marta Nääs,Irenilza de A. Caldara,Fabiana R. Paz,Ibiara C. de L. Almeida Garcia,Rodrigo G. Cordeiro,Alexandra F. S. |
author_role |
author |
author2 |
Nääs,Irenilza de A. Caldara,Fabiana R. Paz,Ibiara C. de L. Almeida Garcia,Rodrigo G. Cordeiro,Alexandra F. S. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Moi,Marta Nääs,Irenilza de A. Caldara,Fabiana R. Paz,Ibiara C. de L. Almeida Garcia,Rodrigo G. Cordeiro,Alexandra F. S. |
dc.subject.por.fl_str_mv |
sound intensity sound attributes animal welfare |
topic |
sound intensity sound attributes animal welfare |
description |
This 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. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162014000300008 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162014000300008 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0100-69162014000300008 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
Engenharia Agrícola v.34 n.3 2014 reponame:Engenharia Agrícola instname:Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA |
instname_str |
Associação Brasileira de Engenharia Agrícola (SBEA) |
instacron_str |
SBEA |
institution |
SBEA |
reponame_str |
Engenharia Agrícola |
collection |
Engenharia Agrícola |
repository.name.fl_str_mv |
Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA) |
repository.mail.fl_str_mv |
revistasbea@sbea.org.br||sbea@sbea.org.br |
_version_ |
1752126271929712640 |