Acoustic Classification of False Killer Whales in the Hawaiian Islands Based on Comprehensive Vocal Repertoire
July 13, 2021
This case study illustrates use of a suite of routines designed to efficiently detect cetacean sounds, extract features, and classify the detection to species using ship-based, visually verified detections of false killer whales.
Use of underwater passive acoustic datasets for species-specific inference requires robust classification systems to identify encounters to species from characteristics of detected sounds. A suite of routines designed to efficiently detect cetacean sounds, extract features, and classify the detection to species is described using ship-based, visually verified detections of false killer whales (Pseudorca crassidens). The best-performing model included features from clicks, whistles, and burst pulses, which correctly classified 99.6% of events. This case study illustrates use of these tools to build classifiers for any group of cetacean species and assess classification confidence when visual confirmation is not available.
McCullough JL, Simonis AE, Sakai T, Oleson EM. 2021. Acoustic classification of false killer whales in the Hawaiian islands based on comprehensive vocal repertoire. JASA Express Letters. 1(7):071201. https://doi.org/10.1121/10.0005512.