Use este identificador para citar ou linkar para este item: https://repositorio.ufrn.br/jspui/handle/123456789/21398
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorTuresson, Hjalmar K.-
dc.contributor.authorRibeiro, Sidarta-
dc.contributor.authorPereira, Danillo R.-
dc.contributor.authorPapa, João P.-
dc.contributor.authorAlbuquerque, Victor Hugo C. de-
dc.date.accessioned2016-09-29T14:47:27Z-
dc.date.available2016-09-29T14:47:27Z-
dc.date.issued2016-09-
dc.identifier.citationTURESSON, H.K.; RIBEIRO, S.; PEREIRA, D.R.; PAPA, J.P.; DE ALBUQUERQUE, V.H.C. Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations. PLoS ONE. v.11, n.9, p.e0163041, 2016. doi:10.1371/journal.pone.0163041pt_BR
dc.identifier.urihttp://hdl.handle.net/123456789/21398-
dc.description.abstractAutomatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available.pt_BR
dc.languageengpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectMachine Learning Algorithmspt_BR
dc.subjectMarmoset Vocalizationspt_BR
dc.titleMachine Learning Algorithmsfor Automatic Classification of Marmoset Vocalizationspt_BR
dc.title.alternativeMachine Learning Algorithmsfor Automatic Classification of Marmoset Vocalizationspt_BR
dc.typearticlept_BR
dc.description.resumoAutomatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available.pt_BR
Aparece nas coleções:ICe - Artigos publicados em periódicos

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
SidartaRibeiro_ICE_2016_MachineLearning.pdf2,43 MBAdobe PDFVisualizar/Abrir


Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.