• Acoustic classification in multifrequency echosounder data using deep convolutional neural networks 

      Brautaset, Olav; Waldeland, Anders Ueland; Johnsen, Espen; Malde, Ketil; Eikvil, Line; Salberg, Arnt-Børre; Handegard, Nils Olav (Journal article; Peer reviewed, 2020)
      Acoustic target classification is the process of assigning observed acoustic backscattering intensity to an acoustic category. A deep learning strategy for acoustic target classification using a convolutional network is ...
    • Final report for the REDUS project - Reduced Uncertainty in Stock Assessment 

      Olsen, Erik Joel Steinar; Aanes, Sondre; Aldrin, Magne Tommy; Breivik, Olav Nikolai; Fuglebakk, Edvin; Goto, Daisuke; Handegard, Nils Olav; Hansen, Cecilie; Holmin, Arne Johannes; Howell, Daniel; Johnsen, Espen; Jourdain, Natoya; Korsbrekke, Knut; Ono, Kotaro; Otterå, Håkon Magne; Perryman, Holly Ann; Subbey, Samuel; Søvik, Guldborg; Umar, Ibrahim; Vatnehol, Sindre; Vølstad, Jon Helge (Rapport fra havforskningen;, Research report, 2021)
      The REDUS project (2016-2020) has been a strategic project at the Institute of Marine Research (IMR) aimed at quantifying and reducing the uncertainty in data-rich and age-structured stock assessments (e.g., cod, herring, ...
    • 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; Ordonez, Alba; Patel, Ruben; Pedersen, Geir; Umar, Ibrahim; Engeland, Tom Van; Vatnehol, Sindre (Rapport fra havforskningen;, Research report, 2021)
      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 ...
    • Machine Learning + Marine Science: Critical Role of Partnerships in Norway 

      Handegard, Nils Olav; Eikvil, Line; Jenssen, Robert; Kampffmeyer, Michael; Salberg, Arnt Børre; Malde, Ketil (Others, 2021)
      In this essay, we review some recent advances in developing machine learning (ML) methods for marine science applications in Norway. We focus mostly on deep learning (DL) methods and review the challenges we have faced in ...
    • Semi-supervised target classification in multi-frequency echosounder data 

      Choi, Changkyu; Kampffmeyer, Michael; Handegard, Nils Olav; Salberg, Arnt Børre; Brautaset, Olav; Eikvil, Line; Jenssen, Robert (Journal article; Peer reviewed, 2021)
      Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem ...