Reference : Finding good acoustic features for parrot vocalizations: The feature generation approach
Scientific journals : Article
Social & behavioral sciences, psychology : Neurosciences & behavior
Life sciences : Zoology
http://hdl.handle.net/2268/105569
Finding good acoustic features for parrot vocalizations: The feature generation approach
English
Giret, Nicolas mailto [Université Paris Ouest Nanterre La Défense > Laboratoire d’Ethologie et Cognition Comparées > > >]
Roy, Pierre [Sony Computer Science Laboratory > > > >]
Albert, Aurélie mailto [Université de Liège - ULg > > > Doct. sc. (biol. orga. & écol. - Bologne)]
Pachet, François [Sony Computer Science Laboratory > > > >]
Kreutzer, Michel [Université Paris Ouest Nanterre La Défense > Laboratoire d’Ethologie et Cognition Comparées > > >]
Bovet, Dalila [Université Paris Ouest Nanterre La Défense > Laboratoire d’Ethologie et Cognition Comparées > > >]
2011
Journal of the Acoustical Society of America
American Institute of Physics
129
1089-1099
Yes (verified by ORBi)
International
0001-4966
1520-8524
Melville
NY
[en] vocalization ; parrot ; call ; spectrography ; bioacoustics
[en] A crucial step in the understanding of vocal behavior of birds is to be able to classify calls in the
repertoire into meaningful types. Methods developed to this aim are limited either because of
human subjectivity or because of methodological issues. The present study investigated whether a feature generation system could categorize vocalizations of a bird species automatically and effectively. This procedure was applied to vocalizations of African gray parrots, known for their capacity to reproduce almost any sound of their environment. Outcomes of the feature generation approach agreed well with a much more labor-intensive process of a human expert classifying based on spectrographic representation, while clearly out-performing other automated methods. The method brings significant improvements in precision over commonly used bioacoustical analyses. As such, the method enlarges the scope of automated, acoustics-based sound classification.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/2268/105569
10.1121/1.3531953

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