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A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach

M. Poupard Orcid Logo, P. Best, Jay Paul Morgan Orcid Logo, G. Pavan, H. Glotin

Royal Society Open Science, Volume: 11, Issue: 11, Start page: 231973

Swansea University Author: Jay Paul Morgan Orcid Logo

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DOI (Published version): 10.1098/rsos.231973

Abstract

The acoustic repertoires of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea are poorly understood. This study aims to create a catalogue of calls, analyse acoustic parameters, and propose a classification tree for future research. An acoustic database was compiled using record...

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Published in: Royal Society Open Science
ISSN: 2054-5703
Published: The Royal Society 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa67703
Abstract: The acoustic repertoires of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea are poorly understood. This study aims to create a catalogue of calls, analyse acoustic parameters, and propose a classification tree for future research. An acoustic database was compiled using recordings from the Alboran Sea, Gulf of Lion, and Ligurian Sea (Western Mediterranean Basin) between 2008 and 2022, totalling 640 calls. Using a deep neural network, the calls were clustered based on frequency contour similarities, leading to the identification of 40 distinct call types defining the local population's vocal repertoire. These categories encompass pulsed calls with varied complexities, from simplistic to highly intricate structures comprising multiple elements and segments.
Keywords: Long-finned pilot whale, vocal repertoire, calls, classification, clustering
College: Faculty of Science and Engineering
Funders: This research was granted by AI Chair on bioacoustics ADSIL ANR-20-CHIA-0014 AID DGA ANR.
Issue: 11
Start Page: 231973

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