[MARMAM] New publication- Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods

Morgan Ziegenhorn mziegenh at ucsd.edu
Thu Apr 28 12:41:15 PDT 2022

Hello there,

I was hoping you could include the following message in the listserv.

Morgan Ziegenhorn


Dear colleagues,

My co-authors and I are excited to announce the publication of the
following article in PLOS One: "Discriminating and classifying odontocete
echolocation clicks in the Hawaiian Islands using machine learning methods"

The full text of the article can be found here:

Authors: Morgan A. Ziegenhorn, Kaitlin E. Frasier, John A. Hildebrand, Erin
M. Oleson, Robin W. Baird, Sean M. Wiggins, Simone Baumann-Pickering

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 Hawaiʻi,
Kauaʻi, 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 Kauaʻi 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 clicks, which is
needed as these datasets continue to grow.

Please contact Morgan Ziegenhorn (mziegenh at ucsd.edu) with any questions.

All the best,

Morgan Ziegenhorn
PhD Candidate
Scripps Acoustic Ecology Lab and Scripps Whale Acoustics Lab
Scripps Institution of Oceanography

"Say to them [...] ' Even if you are not ready for day, it cannot always be
night.' " - Gwendolyn Brooks
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