Fisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acoustics

Handegard, Nils Olav; Andersen, Lars Nonboe; Brautaset, Olav; Choi, Changkyu; Eliassen, Inge Kristian; Heggelund, Yngve; Hestnes, Arne Johan; Malde, Ketil; Osland, Håkon; Ordoñez, Alba; Patel, Ruben; Pedersen, Geir; Umar, Ibrahim; Engeland, Tom Van; Vatnehol, Sindre

Publikasjonsdetaljer

Utgivere: Havforskningsinstituttet

Serie: Rapport fra havforskningen 2021 - 25

År: 2021

Lenker:
FULLTEKST: www.hi.no/hi/nettrapporter/rapport-fra-havforskningen-en-2021-25
ARKIV: hdl.handle.net/11250/2760192

This 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.