Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods

• Main Authors: Ziegenhorn, Morgan A, Frasier, Kaitlin E, Hildebrand, John A, Oleson, Erin M, Baird, Robin W, Wiggins, Sean M, Baumann-Pickering, Simone
• Other Authors: Halliday, William David
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2022
• Subjects: Biological Sciences; Ecology; Environmental Sciences; Information and Computing Sciences; Machine Learning; Life Below Water; Acoustics; Animals; Cetacea; Dolphins; Echolocation; Fin Whale; Hawaii; Islands; Sound Spectrography; Vocalization; Animal; General Science & Technology; Fin whale; toothed whale; toothed whales
• Online Access: https://escholarship.org/uc/item/7668d3kc

Passive acoustic monitoring (PAM) has proven a powerful tool for the study of marine mammals, allowing for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. From 2008-2019, a set of PAM recordings covering the frequency band of most toothed whale (odontocete) echolocation clicks were collected at sites off the islands of Hawai'i, Kaua'i, and Pearl and Hermes Reef. However, due to the size of this dataset and the complexity of species-level acoustic classification, multi-year, multi-species analyses had not yet been completed. This study shows how a machine learning toolkit can effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Using these methods, it was possible to distill ten unique echolocation click 'types' attributable to regional odontocetes at the genus or species level. In one case, auxiliary sightings and recordings were used to attribute a new click type to the rough-toothed dolphin, Steno bredanensis. Types defined by clustering were then used as input classes in a neural-network based classifier, which was trained, tested, and evaluated on 5-minute binned data segments. Network precision was variable, with lower precision occurring most notably for false killer whales, Pseudorca crassidens, across all sites (35-76%). However, accuracy and recall were high (>96% and >75%, respectively) in all cases except for one type of short-finned pilot whale, Globicephala macrorhynchus, call class at Kaua'i and Pearl and Hermes Reef (recall >66%). These results emphasize the utility of machine learning in analysis of large PAM datasets. The classifier and timeseries developed here will facilitate further analyses of spatiotemporal patterns of included toothed whales. Broader application of these methods may improve the efficiency of global multi-species PAM data processing for echolocation ...