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• W1992076204 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." @default.
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• W1992076204 date "2011-02-01" @default.
• W1992076204 modified "2023-10-17" @default.
• W1992076204 title "Finding good acoustic features for parrot vocalizations: The feature generation approach" @default.
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• W1992076204 doi "https://doi.org/10.1121/1.3531953" @default.
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