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dc.contributor.authorHandegard, Nils Olav
dc.contributor.authorAndersen, Lars Nonboe
dc.contributor.authorBrautaset, Olav
dc.contributor.authorChoi, Changkyu
dc.contributor.authorEliassen, Inge Kristian
dc.contributor.authorHeggelund, Yngve
dc.contributor.authorHestnes, Arne Johan
dc.contributor.authorMalde, Ketil
dc.contributor.authorOsland, Håkon
dc.contributor.authorOrdonez, Alba
dc.contributor.authorPatel, Ruben
dc.contributor.authorPedersen, Geir
dc.contributor.authorUmar, Ibrahim
dc.contributor.authorEngeland, Tom Van
dc.contributor.authorVatnehol, Sindre
dc.date.accessioned2021-08-26T06:55:03Z
dc.date.available2021-08-26T06:55:03Z
dc.date.issued2021-06-15
dc.description.abstractThis report documents a workshop organised by the COGMAR and CRIMAC projects. The objective of the workshop was twofold. The first objective was to give an overview of ongoing work using machine learning for Acoustic Target Classification (ATC). Machine learning methods, and in particular deep learning models, are currently being used across a range of different fields, including ATC. The objective was to give an overview of the status of the work. The second objective was to familiarise participants with machine learning background to fisheries acoustics and to discuss a way forward towards a standard framework for sharing data and code. This includes data standards, standard processing steps and algorithms for efficient access to data for machine learning frameworks. The results from the discussion contributes to the process in ICES for developing a community standard for fisheries acoustics data.en_US
dc.descriptionSource at <a href=https://www.hi.no/hi/nettrapporter/rapport-fra-havforskningen-en-2021-25>https://www.hi.no/hi/nettrapporter/rapport-fra-havforskningen-en-2021-25</a>en_US
dc.identifier.citationHandegard NO, Andersen LN, Brautaset O, Choi C, Eliassen IK, Heggelund Y, Hestnes AJ, Malde K, Osland, Ordonez A, Patel R, Pedersen G, Umar I, Engeland TV, Vatnehol S. Fisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acoustics. Havforskningsinstituttet; 2021. 25 p.. Rapport fra havforskningen(2021 - 25)en_US
dc.identifier.cristinIDFRIDAID 1915855
dc.identifier.urihttps://hdl.handle.net/10037/22246
dc.language.isoengen_US
dc.publisherHavforskningsinstitutteten_US
dc.relation.ispartofseriesRapport fra havforskningen ; 2021 - 25en_US
dc.relation.urihttps://www.hi.no/hi/nettrapporter/rapport-fra-havforskningen-en-2021-25
dc.rights.accessRightsopenAccessen_US
dc.rights.holderKopirett © 2021 Havforskningsinstituttet. Alle rettigheter reserverten_US
dc.subjectVDP::Agriculture and fishery disciplines: 900::Fisheries science: 920en_US
dc.subjectVDP::Landbruks- og Fiskerifag: 900::Fiskerifag: 920en_US
dc.title.alternativeFiskeriakustikk og akustisk målklassifisering - Rapport frå COGMAR/CRIMAC arbeidsmøte om maskinlæring og fiskeriakustikken_US
dc.titleFisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acousticsen_US
dc.typeResearch reporten_US
dc.typeForskningsrapporten_US


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