title: Finding good acoustic features for parrot vocalizations: the feature generation approach.
abstract: 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.
authors.affiliation: Laboratoire d'Ethologie et Cognition Comparées, Université Paris Ouest Nanterre La Défense, 200 avenue de la République, 92000 Nanterre, France. ngiret@u-paris10.fr
authors.forename: Nicolas
authors.initials: N
authors.lastname: Giret
authors.forename: Pierre
authors.initials: P
authors.lastname: Roy
authors.forename: Aurélie
authors.initials: A
authors.lastname: Albert
authors.forename: François
authors.initials: F
authors.lastname: Pachet
authors.forename: Michel
authors.initials: M
authors.lastname: Kreutzer
authors.forename: Dalila
authors.initials: D
authors.lastname: Bovet
mesh_terms: D000162:Acoustics; D000818:Animals; D001331:Automation; D005260:Female; D008297:Male; D010311:Parrots / Q000502:physiology*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D014828:Vocalization, Animal / Q000145:classification