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dc.contributor.authorCarpentier, Lenn
dc.contributor.authorBerckmans, Daniel
dc.contributor.authorYoussef, Ali
dc.contributor.authorBerckmans, Dries
dc.contributor.authorvan Waterschoot, Toon
dc.contributor.authorJohnston, Dayle
dc.contributor.authorFerguson, Natasha
dc.contributor.authorEarley, Bernadette
dc.contributor.authorFontana, Ilaria
dc.contributor.authorTullo, Emanuela
dc.contributor.authorGuarino, Marcella
dc.contributor.authorVranken, Erik
dc.contributor.authorNorton, Tomas
dc.date.accessioned2020-07-03T10:51:48Z
dc.date.available2020-07-03T10:51:48Z
dc.date.issued2018-07-06
dc.identifier.citationCarpentier, L., Berckmans, D., Youssef, A., Berckmans, D., van Waterschoot, T., Johnston, D., Ferguson, N., Earley, B., Fontana, I., Tullo, E., Guarino, M., Vranken, E. and Norton, T. Automatic cough detection for bovine respiratory disease in a calf house. Biosystems Engineering, 2018, 173, 45-56. doi: https://doi.org/10.1016/j.biosystemseng.2018.06.018en_US
dc.identifier.issn1537-5110
dc.identifier.urihttp://hdl.handle.net/11019/2141
dc.descriptionpeer-revieweden_US
dc.description.abstractIn calf rearing, bovine respiratory disease (BRD) is a major animal health challenge. Farmers incur severe economic losses due to BRD. Additional to economic costs, outbreaks of BRD impair the welfare of the animal and extra expertise and labour are needed to treat and care for the infected animals. Coughing is recognised as a clinical manifestation of BRD. Therefore, the monitoring of coughing in a calf house has the potential to detect cases of respiratory infection before they become too severe, and thus to limit the impact of BRD on both the farmer and the animal. The objective of this study was to develop an algorithm for detection of coughing sounds in a calf house. Sounds were recorded in four adjacent compartments of one calf house over two time periods (82 and 96 days). There were approximately 21 and 14 calves in each compartment over the two time-periods, respectively. The algorithm was developed using 445 min of sound data. These data contained 664 different cough references, which were labelled by a human expert. It was found that, during the first time period in all 3 of the compartments and during the second period in 2 out of 4 compartments, the algorithm worked very well (precision higher than 80%), while in the 2 other cases the algorithm worked well but the precision was less (66.6% and 53.8%). A relation between the number of calves diagnosed with BRD and the detected coughs is shown.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesBiosystems Engineering;Vol. 173
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectPrecision Livestock Farmingen_US
dc.subjectBioacousticsen_US
dc.subjectSound analysisen_US
dc.subjectCoughen_US
dc.subjectCalvesen_US
dc.titleAutomatic cough detection for bovine respiratory disease in a calf houseen_US
dc.typeArticleen_US
dc.embargo.terms2020-07-06en_US
dc.identifier.doihttps://doi.org/10.1016/j.biosystemseng.2018.06.018
dc.contributor.sponsorEuropean Unionen_US
dc.contributor.sponsorGrantNumber311825en_US
dc.source.volume173
dc.source.beginpage45-56


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