Acoustic classification of false killer whales in the Hawaiian islands based on comprehensive vocal repertoire

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.