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

ABSTRACT: 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 unsupervis

SUBMITTER: Ziegenhorn MA 

PROVIDER: S-EPMC9004765 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature