ORBi: Giret Nicolas - Finding good acoustic features for parrot vocalizations: The feature generation approach

Reference : Finding good acoustic features for parrot vocalizations: The feature generation approach


	Document type :	Scientific journals : Article
	Discipline(s) :	Social & behavioral sciences, psychology : Neurosciences & behavior Life sciences : Zoology
	To cite this reference:	http://hdl.handle.net/2268/105569

Title :	Finding good acoustic features for parrot vocalizations: The feature generation approach
Language :	English
Author, co-author :	Giret, Nicolas [Université Paris Ouest Nanterre La Défense > Laboratoire d’Ethologie et Cognition Comparées > > >]
	Roy, Pierre [Sony Computer Science Laboratory > > > >]
	Albert, Aurélie [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 > > >]
Publication date :	2011
Journal title :	Journal of the Acoustical Society of America
Publisher :	American Institute of Physics
Volume :	129
Pages :	1089-1099
Audience :	International
ISSN :	0001-4966
e-ISSN :	1520-8524
City :	Melville
Country :	NY
Keywords :	[en] vocalization ; parrot ; call ; spectrography ; bioacoustics
Abstract :	[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.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/2268/105569
DOI :	10.1121/1.3531953

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