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- W4254897453 abstract "Dividing a species acoustic repertoire into biologically relevant categories presents a widespread problem in the study of animal sound communication. Such categorization is fundamental to any attempt to compare repertoires between contexts, individuals, populations or species. Automated procedures allow rapid, repeatable and objective categorization, however, in the past they have often performed poorly at detecting biologically meaningful sound classes. Arguably this is because automated methods have often failed to address the nonlinearities of animal sound perception and a method that incorporates dynamic time warping and an adaptive resonance theory (ART) neural network to avoid some of the problems is presented here. The method was tested on 104 randomly chosen whistles recorded from four captive bottlenose dolphins (Tursiops truncatus). This data set includes known biologically meaningful categories in the form of 42 distinctive stereotyped whistles produced when each individual was kept in isolation. The automated procedure correctly grouped all but two of the stereotyped whistles into their respective categories, and thus performed as good as human observers. However, compared to humans, the automated system provided finer categorization of the remaining non-stereotyped whistles. These results suggest that this methodology provides a repeatable and objective means of splitting bioacoustic signals into biologically meaningful categories." @default.
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- W4254897453 date "2005-04-01" @default.
- W4254897453 modified "2023-10-18" @default.
- W4254897453 title "Automated categorization of bioacoustic signals: Avoiding perceptual pitfalls" @default.
- W4254897453 doi "https://doi.org/10.1121/1.4787454" @default.
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