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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HAL CCSD
2019
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Online Access: | https://hal.archives-ouvertes.fr/hal-02445426/file/poupard2019_orcalab_ocean.pdf https://hal.archives-ouvertes.fr/hal-02445426 |
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fttriple:oai:gotriple.eu:10670/1.gfocs8 2023-05-15T17:53:34+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) 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) 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) Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) PNRIA Marseille, France 2019-06-17 https://hal.archives-ouvertes.fr/hal-02445426/file/poupard2019_orcalab_ocean.pdf https://hal.archives-ouvertes.fr/hal-02445426 en eng HAL CCSD hal-02445426 10670/1.gfocs8 https://hal.archives-ouvertes.fr/hal-02445426/file/poupard2019_orcalab_ocean.pdf https://hal.archives-ouvertes.fr/hal-02445426 other Hyper Article en Ligne - Sciences de l'Homme et de la Société OCEANS OCEANS, Jun 2019, Marseille, France Ethoacoustics Deep Learning Convolutional Neural Networks Orcas Orcinus orca Cetaceans Bioacoustics Environmental factors Soundscape Big data geo envir Conference Output https://vocabularies.coar-repositories.org/resource_types/c_c94f/ 2019 fttriple 2023-01-22T16:37:22Z 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. Other/Unknown Material Orca Orcinus orca Unknown Canada |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
English |
topic |
Ethoacoustics Deep Learning Convolutional Neural Networks Orcas Orcinus orca Cetaceans Bioacoustics Environmental factors Soundscape Big data geo envir |
spellingShingle |
Ethoacoustics Deep Learning Convolutional Neural Networks Orcas Orcinus orca Cetaceans Bioacoustics Environmental factors Soundscape Big data geo envir 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 geo envir |
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) 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) 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) Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) PNRIA |
format |
Other/Unknown Material |
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/file/poupard2019_orcalab_ocean.pdf https://hal.archives-ouvertes.fr/hal-02445426 |
op_coverage |
Marseille, France |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Orca Orcinus orca |
genre_facet |
Orca Orcinus orca |
op_source |
Hyper Article en Ligne - Sciences de l'Homme et de la Société OCEANS OCEANS, Jun 2019, Marseille, France |
op_relation |
hal-02445426 10670/1.gfocs8 https://hal.archives-ouvertes.fr/hal-02445426/file/poupard2019_orcalab_ocean.pdf https://hal.archives-ouvertes.fr/hal-02445426 |
op_rights |
other |
_version_ |
1766161275629338624 |