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A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach
Royal Society Open Science, Volume: 11, Issue: 11, Start page: 231973
Swansea University Author:
Jay Paul Morgan
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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...
Published in: | Royal Society Open Science |
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ISSN: | 2054-5703 |
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The Royal Society
2024
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URI: | https://cronfa.swan.ac.uk/Record/cronfa67703 |
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2025-02-05T15:46:58.6886374 v2 67703 2024-09-17 A first vocal repertoire characterization of long-finned pilot whales (Globicephala melas) in the Mediterranean Sea: a machine learning approach df9a27bcf77b4769c2ebbb702b587491 0000-0003-3719-362X Jay Paul Morgan Jay Paul Morgan true false 2024-09-17 MACS 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. Journal Article Royal Society Open Science 11 11 231973 The Royal Society 2054-5703 Long-finned pilot whale, vocal repertoire, calls, classification, clustering 6 11 2024 2024-11-06 10.1098/rsos.231973 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee This research was granted by AI Chair on bioacoustics ADSIL ANR-20-CHIA-0014 AID DGA ANR. 2025-02-05T15:46:58.6886374 2024-09-17T12:43:49.2951334 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science M. Poupard 0000-0002-8235-5184 1 P. Best 2 Jay Paul Morgan 0000-0003-3719-362X 3 G. Pavan 4 H. Glotin 5 67703__33075__8812b7933fc744268ce8d73fc5819f11.pdf 67703.VOR.pdf 2024-12-06T11:35:42.6091778 Output 2352672 application/pdf Version of Record true © 2024 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License (CC-BY 4.0). true eng http://creativecommons.org/licenses/by/4.0/ |
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 |
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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 |
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Journal article |
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Royal Society Open Science |
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11 |
container_issue |
11 |
container_start_page |
231973 |
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2024 |
institution |
Swansea University |
issn |
2054-5703 |
doi_str_mv |
10.1098/rsos.231973 |
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The Royal Society |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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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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11.067264 |