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

The acoustic repertoires of long-finned pilot whales (<i>Globicephala melas</i>) 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. This study marks the initial documentation of the vocal catalogue of long-finned pilot whales in the Mediterranean Sea. Subsequent research should delve deeper into this multifaceted communication system and explore its potential linkages with social structures.

Authors

Institutions

Publication Details

Journal
Royal Society Open Science
Published
2024-11-01
DOI
https://doi.org/10.1098/rsos.231973
Citations
4
Primary Topic
Marine animal studies overview
Type
article
Field-Weighted Citation Impact
1.84

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Hervé Glotin, Marion Poupard, Gianni Pavan, P. N. Best et al.
4 citations
Royal Society Open Science
Marine animal studies overview
1.84
article

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

Hervé Glotin, Marion Poupard, Gianni Pavan, P. N. Best, Jay Morgan
article en
4 citations

Abstract

The acoustic repertoires of long-finned pilot whales (<i>Globicephala melas</i>) 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. This study marks the initial documentation of the vocal catalogue of long-finned pilot whales in the Mediterranean Sea. Subsequent research should delve deeper into this multifaceted communication system and explore its potential linkages with social structures.

Royal Society Open ScienceVol. 11(11)
Centre National de la Recherche Scientifique (FR), Université de Toulon (FR), University of Pavia (IT), Swansea University (GB), Laboratoire d’Informatique et Systèmes (FR)
University of St Andrews, Agence Nationale de la Recherche, Università degli Studi di Pavia, Institut Universitaire de France, Prince Albert II of Monaco Foundation, Office of Naval Research
Life below water
Openalex Percentile: Top 14%
Marine animal studies overview
1.84
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.