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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
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title A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach
spellingShingle A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach
Jay Paul Morgan
title_short A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach
title_full A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach
title_fullStr A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach
title_full_unstemmed A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach
title_sort A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach
author_id_str_mv df9a27bcf77b4769c2ebbb702b587491
author_id_fullname_str_mv df9a27bcf77b4769c2ebbb702b587491_***_Jay Paul Morgan
author Jay Paul Morgan
author2 M. Poupard
P. Best
Jay Paul Morgan
G. Pavan
H. Glotin
format Journal article
container_title Royal Society Open Science
container_volume 11
container_issue 11
container_start_page 231973
publishDate 2024
institution Swansea University
issn 2054-5703
doi_str_mv 10.1098/rsos.231973
publisher The Royal Society
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description 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.
published_date 2024-11-06T07:37:06Z
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