Automated categorization of bioacoustic signals: Avoiding perceptual pitfalls

Abstract

Dividing the acoustic repertoires of animals into biologically relevant categories presents a widespread problem in the study of animal sound communication, essential to any comparison of repertoires between contexts, individuals, populations, or species. Automated procedures allow rapid, repeatable, and objective categorization, but often perform poorly at detecting biologically meaningful sound classes. Arguably this is because many automated methods fail to address the nonlinearities of animal sound perception. We present a new method of categorization that incorporates dynamic time-warping and an adaptive resonance theory (ART) neural network. This method was tested on 104 randomly chosen whistle contours from four captive bottlenose dolphins (Tursiops truncatus), as well as 50 frequency contours extracted from calls of transient killer whales (Orcinus orca). The dolphin data included known biologically meaningful categories in the form of 42 stereotyped whistles produced when each individual was isolated from its group. The automated procedure correctly grouped all but two stereotyped whistles into separate categories, thus performing as well as human observers. The categorization of killer whale calls largely corresponded to visual and aural categorizations by other researchers. These results suggest that this methodology provides a repeatable and objective means of dividing bioacoustic signals into biologically meaningful categories.