Deep Learning for Ethoacoustics of Orcas on three years pentaphonic continuous recording at Orcalab revealing tide, moon and diel effects
International audience One of the best ways of studying animals that produce signals in underwater environments is to use passive acoustic monitoring (PAM). Acoustic monitoring is used to study marine mammals in oceans, and gives us information for understanding cetacean life, such as their behaviou...
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ftccsdartic:oai:HAL:hal-02445426v1 2023-05-15T17:53:36+02:00 Deep Learning for Ethoacoustics of Orcas on three years pentaphonic continuous recording at Orcalab revealing tide, moon and diel effects Poupard, Marion Best, Paul Schlüter, Jan Prévot, Jean-Marc Symonds, Helena Spong, Paul Glotin, Hervé Laboratoire des Sciences de l'Information et des Systèmes (LSIS) Centre National de la Recherche Scientifique (CNRS)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Université de Toulon (UTLN)-Aix Marseille Université (AMU) DYNamiques de l’Information (DYNI) Laboratoire d'Informatique et Systèmes (LIS) 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) Représentations musicales (Repmus) Sciences et Technologies de la Musique et du Son (STMS) Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU) PNRIA Marseille, France 2019-06-17 https://hal.archives-ouvertes.fr/hal-02445426 https://hal.archives-ouvertes.fr/hal-02445426/document https://hal.archives-ouvertes.fr/hal-02445426/file/poupard2019_orcalab_ocean.pdf en eng HAL CCSD hal-02445426 https://hal.archives-ouvertes.fr/hal-02445426 https://hal.archives-ouvertes.fr/hal-02445426/document https://hal.archives-ouvertes.fr/hal-02445426/file/poupard2019_orcalab_ocean.pdf info:eu-repo/semantics/OpenAccess OCEANS https://hal.archives-ouvertes.fr/hal-02445426 OCEANS, Jun 2019, Marseille, France Ethoacoustics Deep Learning Convolutional Neural Networks Orcas Orcinus orca Cetaceans Bioacoustics Environmental factors Soundscape Big data [SDE]Environmental Sciences [SDE.BE]Environmental Sciences/Biodiversity and Ecology [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] info:eu-repo/semantics/conferenceObject Conference papers 2019 ftccsdartic 2021-12-12T01:58:37Z International audience One of the best ways of studying animals that produce signals in underwater environments is to use passive acoustic monitoring (PAM). Acoustic monitoring is used to study marine mammals in oceans, and gives us information for understanding cetacean life, such as their behaviour, movement or reproduction. Automated analysis for captured sound is almost essential because of the large quantity of data. A deep learning approach was chosen for this task, since it has proven great efficiency for answering such problematics. This study focused on the orcas (Orcinus orca) of northern Vancouver Island, Canada, in collaboration with the NGO Orcalab which developed a multi-hydrophone recording station around Hanson Island to study orcas. The acoustic station is composed of 5 hydrophones and extends over 50 km 2 of ocean. Since 2016 we are continuously streaming the hydrophone signals to our laboratory at Toulon, France, yielding nearly 50 TB of synchronous multichannel recordings. The objective for this research is to do a preliminary analysis of the collected data and demonstrate influence of environmental factors (tidal, moon phase and daily period) on the orcas' acoustic activities. Conference Object Orca Orcinus orca Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Canada |
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 |
Ethoacoustics Deep Learning Convolutional Neural Networks Orcas Orcinus orca Cetaceans Bioacoustics Environmental factors Soundscape Big data [SDE]Environmental Sciences [SDE.BE]Environmental Sciences/Biodiversity and Ecology [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
spellingShingle |
Ethoacoustics Deep Learning Convolutional Neural Networks Orcas Orcinus orca Cetaceans Bioacoustics Environmental factors Soundscape Big data [SDE]Environmental Sciences [SDE.BE]Environmental Sciences/Biodiversity and Ecology [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Poupard, Marion Best, Paul Schlüter, Jan Prévot, Jean-Marc Symonds, Helena Spong, Paul Glotin, Hervé Deep Learning for Ethoacoustics of Orcas on three years pentaphonic continuous recording at Orcalab revealing tide, moon and diel effects |
topic_facet |
Ethoacoustics Deep Learning Convolutional Neural Networks Orcas Orcinus orca Cetaceans Bioacoustics Environmental factors Soundscape Big data [SDE]Environmental Sciences [SDE.BE]Environmental Sciences/Biodiversity and Ecology [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
description |
International audience One of the best ways of studying animals that produce signals in underwater environments is to use passive acoustic monitoring (PAM). Acoustic monitoring is used to study marine mammals in oceans, and gives us information for understanding cetacean life, such as their behaviour, movement or reproduction. Automated analysis for captured sound is almost essential because of the large quantity of data. A deep learning approach was chosen for this task, since it has proven great efficiency for answering such problematics. This study focused on the orcas (Orcinus orca) of northern Vancouver Island, Canada, in collaboration with the NGO Orcalab which developed a multi-hydrophone recording station around Hanson Island to study orcas. The acoustic station is composed of 5 hydrophones and extends over 50 km 2 of ocean. Since 2016 we are continuously streaming the hydrophone signals to our laboratory at Toulon, France, yielding nearly 50 TB of synchronous multichannel recordings. The objective for this research is to do a preliminary analysis of the collected data and demonstrate influence of environmental factors (tidal, moon phase and daily period) on the orcas' acoustic activities. |
author2 |
Laboratoire des Sciences de l'Information et des Systèmes (LSIS) Centre National de la Recherche Scientifique (CNRS)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Université de Toulon (UTLN)-Aix Marseille Université (AMU) DYNamiques de l’Information (DYNI) Laboratoire d'Informatique et Systèmes (LIS) 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) Représentations musicales (Repmus) Sciences et Technologies de la Musique et du Son (STMS) Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU) PNRIA |
format |
Conference Object |
author |
Poupard, Marion Best, Paul Schlüter, Jan Prévot, Jean-Marc Symonds, Helena Spong, Paul Glotin, Hervé |
author_facet |
Poupard, Marion Best, Paul Schlüter, Jan Prévot, Jean-Marc Symonds, Helena Spong, Paul Glotin, Hervé |
author_sort |
Poupard, Marion |
title |
Deep Learning for Ethoacoustics of Orcas on three years pentaphonic continuous recording at Orcalab revealing tide, moon and diel effects |
title_short |
Deep Learning for Ethoacoustics of Orcas on three years pentaphonic continuous recording at Orcalab revealing tide, moon and diel effects |
title_full |
Deep Learning for Ethoacoustics of Orcas on three years pentaphonic continuous recording at Orcalab revealing tide, moon and diel effects |
title_fullStr |
Deep Learning for Ethoacoustics of Orcas on three years pentaphonic continuous recording at Orcalab revealing tide, moon and diel effects |
title_full_unstemmed |
Deep Learning for Ethoacoustics of Orcas on three years pentaphonic continuous recording at Orcalab revealing tide, moon and diel effects |
title_sort |
deep learning for ethoacoustics of orcas on three years pentaphonic continuous recording at orcalab revealing tide, moon and diel effects |
publisher |
HAL CCSD |
publishDate |
2019 |
url |
https://hal.archives-ouvertes.fr/hal-02445426 https://hal.archives-ouvertes.fr/hal-02445426/document https://hal.archives-ouvertes.fr/hal-02445426/file/poupard2019_orcalab_ocean.pdf |
op_coverage |
Marseille, France |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Orca Orcinus orca |
genre_facet |
Orca Orcinus orca |
op_source |
OCEANS https://hal.archives-ouvertes.fr/hal-02445426 OCEANS, Jun 2019, Marseille, France |
op_relation |
hal-02445426 https://hal.archives-ouvertes.fr/hal-02445426 https://hal.archives-ouvertes.fr/hal-02445426/document https://hal.archives-ouvertes.fr/hal-02445426/file/poupard2019_orcalab_ocean.pdf |
op_rights |
info:eu-repo/semantics/OpenAccess |
_version_ |
1766161299141558272 |