Developing a click type “library” for Hawaiian odontocetes using machine learning methods

Abstract

Though odontocete echolocation clicks are highly useful in passive acoustic monitoring, their acoustic features vary depending on behavior, orientation, and location relative to the receiver. Using unsupervised machine learning, regional sets of echolocation click types can be identified that allow for automated labeling of bioacoustic data while accounting for within-type signal variability. This methodology derives labels for clusters of click types based on parameters including spectral shape, inter-click interval, and click duration. Here we present classification results of echolocation clicks produced by Hawaiian odontocetes. Unsupervised methods were used to establish a set of click types for 10 years of recordings (200 kHz) collected from a bottom-mounted hydrophone off the coast of Kona, HI. These automated labels were compared with manual labels for species with established and recognizable clicks, which allowed for ground-truthing of some click types identified by the unsupervised machine learning method. Towed array data (500 kHz) was used to assign click types to species with visual verification, expanding the set of known species-specific click types for the region. The click types and method established here will be useful in future analyses of Hawaiian odontocete density estimation, abundance, and distribution patterns using acoustic data without requiring visual confirmation.