Automated Detection and Classification of Cetacean Acoustic Signals
Cetaceans make an important use of acoustics to socialise, travel and hunt. Therefore, their monitoring via passive acoustics allows to increase our knowledge on these species, some of which are endangered. This approach generates large amounts of data which motivates the development of automatic pr...
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2022
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ftccsdartic:oai:HAL:tel-03826638v1 2023-05-15T16:13:19+02:00 Automated Detection and Classification of Cetacean Acoustic Signals Détection et classification automatiques de signaux acoustiques de cétacés Best, Paul DYNamiques de l’Information (DYNI) Laboratoire d'Informatique et Systèmes (LIS) Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) Université de toulon Hervé Glotin Ricard Marxer Sébastien Paris ANR-20-CHIA-0014,ADSIL,Écoute intelligente sous-marine avancée(2020) ANR-18-CE40-0014,SMILES,Modélisation et Inférence Statistique pour l'Apprentissage non-supervisé à partir de Données Massives(2018) European Project: 2022-09-30 https://hal.science/tel-03826638 https://hal.science/tel-03826638/document https://hal.science/tel-03826638/file/2022___PhD_these___Paul_Best.pdf en eng HAL CCSD tel-03826638 https://hal.science/tel-03826638 https://hal.science/tel-03826638/document https://hal.science/tel-03826638/file/2022___PhD_these___Paul_Best.pdf info:eu-repo/semantics/OpenAccess https://hal.science/tel-03826638 Machine Learning [cs.LG]. Université de toulon, 2022. English. ⟨NNT : ⟩ Bioacoustic Cetaceans Artificial neural netwoks Bioacoustique Cétacés Réseaux de neurones artificiels [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [SDV.BA]Life Sciences [q-bio]/Animal biology [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing info:eu-repo/semantics/doctoralThesis Theses 2022 ftccsdartic 2023-02-12T11:57:15Z Cetaceans make an important use of acoustics to socialise, travel and hunt. Therefore, their monitoring via passive acoustics allows to increase our knowledge on these species, some of which are endangered. This approach generates large amounts of data which motivates the development of automatic procedures. Neural networks represent an opportunity for this task, having already shown great performances for image classification or speech recognition. The work of this thesis is in three folds: data annotation, neural network training, and model application. Different methods are first proposed to speed up the annotation process depending on the type of target signal and the available data. This work allowed to build training databases for the detection of 5 types of signals (sperm whale clicks, fin whale 20Hz pulses, killer whale vocalisations, delphinid vocalizations, and humpback whale calls). The resulting models have first allowed the development of an embedded real time alert system for the reduction of collision risks with ferries. Then, the analysis of long term data showed sperm whale presence patterns in relation to anthropogenic noise, and revealed the song structure of the Mediterranean fin whale with an evolution over 20 years. Finally, a modelling of the orcas communication system in BritishColumbia was carried out using vocalisation detection and classification models. Les cétacés font un usage important de l’acoustique pour socialiser, se déplacer et chasser. De ce fait, leur suivi par l’acoustique passive permet d’accroître nos connaissances sur ces espèces dont certaines sont en voie de disparition. Cette approche génère de grandes quantités de données qui motive le développement de procédures automatiques pour les traiter. Les réseaux neuronaux représentent une opportunité pour cette tâche, ayant déjà démontré de grandes performances pour la classification d’image ou encore la reconnaissance de parole. Les travaux de cette thèse sont articulés en trois parties: l’annotation de données, ... Doctoral or Postdoctoral Thesis Fin whale Humpback Whale Killer Whale Sperm whale Killer whale Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
Bioacoustic Cetaceans Artificial neural netwoks Bioacoustique Cétacés Réseaux de neurones artificiels [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [SDV.BA]Life Sciences [q-bio]/Animal biology [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing |
spellingShingle |
Bioacoustic Cetaceans Artificial neural netwoks Bioacoustique Cétacés Réseaux de neurones artificiels [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [SDV.BA]Life Sciences [q-bio]/Animal biology [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Best, Paul Automated Detection and Classification of Cetacean Acoustic Signals |
topic_facet |
Bioacoustic Cetaceans Artificial neural netwoks Bioacoustique Cétacés Réseaux de neurones artificiels [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [SDV.BA]Life Sciences [q-bio]/Animal biology [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing |
description |
Cetaceans make an important use of acoustics to socialise, travel and hunt. Therefore, their monitoring via passive acoustics allows to increase our knowledge on these species, some of which are endangered. This approach generates large amounts of data which motivates the development of automatic procedures. Neural networks represent an opportunity for this task, having already shown great performances for image classification or speech recognition. The work of this thesis is in three folds: data annotation, neural network training, and model application. Different methods are first proposed to speed up the annotation process depending on the type of target signal and the available data. This work allowed to build training databases for the detection of 5 types of signals (sperm whale clicks, fin whale 20Hz pulses, killer whale vocalisations, delphinid vocalizations, and humpback whale calls). The resulting models have first allowed the development of an embedded real time alert system for the reduction of collision risks with ferries. Then, the analysis of long term data showed sperm whale presence patterns in relation to anthropogenic noise, and revealed the song structure of the Mediterranean fin whale with an evolution over 20 years. Finally, a modelling of the orcas communication system in BritishColumbia was carried out using vocalisation detection and classification models. Les cétacés font un usage important de l’acoustique pour socialiser, se déplacer et chasser. De ce fait, leur suivi par l’acoustique passive permet d’accroître nos connaissances sur ces espèces dont certaines sont en voie de disparition. Cette approche génère de grandes quantités de données qui motive le développement de procédures automatiques pour les traiter. Les réseaux neuronaux représentent une opportunité pour cette tâche, ayant déjà démontré de grandes performances pour la classification d’image ou encore la reconnaissance de parole. Les travaux de cette thèse sont articulés en trois parties: l’annotation de données, ... |
author2 |
DYNamiques de l’Information (DYNI) Laboratoire d'Informatique et Systèmes (LIS) Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) Université de toulon Hervé Glotin Ricard Marxer Sébastien Paris ANR-20-CHIA-0014,ADSIL,Écoute intelligente sous-marine avancée(2020) ANR-18-CE40-0014,SMILES,Modélisation et Inférence Statistique pour l'Apprentissage non-supervisé à partir de Données Massives(2018) European Project: |
format |
Doctoral or Postdoctoral Thesis |
author |
Best, Paul |
author_facet |
Best, Paul |
author_sort |
Best, Paul |
title |
Automated Detection and Classification of Cetacean Acoustic Signals |
title_short |
Automated Detection and Classification of Cetacean Acoustic Signals |
title_full |
Automated Detection and Classification of Cetacean Acoustic Signals |
title_fullStr |
Automated Detection and Classification of Cetacean Acoustic Signals |
title_full_unstemmed |
Automated Detection and Classification of Cetacean Acoustic Signals |
title_sort |
automated detection and classification of cetacean acoustic signals |
publisher |
HAL CCSD |
publishDate |
2022 |
url |
https://hal.science/tel-03826638 https://hal.science/tel-03826638/document https://hal.science/tel-03826638/file/2022___PhD_these___Paul_Best.pdf |
genre |
Fin whale Humpback Whale Killer Whale Sperm whale Killer whale |
genre_facet |
Fin whale Humpback Whale Killer Whale Sperm whale Killer whale |
op_source |
https://hal.science/tel-03826638 Machine Learning [cs.LG]. Université de toulon, 2022. English. ⟨NNT : ⟩ |
op_relation |
tel-03826638 https://hal.science/tel-03826638 https://hal.science/tel-03826638/document https://hal.science/tel-03826638/file/2022___PhD_these___Paul_Best.pdf |
op_rights |
info:eu-repo/semantics/OpenAccess |
_version_ |
1765998977153499136 |