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Yolov8 model weights to detect unknown underwater sounds
Citable as data publication
Parcericas, C.; Schall, E.; te Velde, K.; Botteldooren, D.; Devos, P.; Debusschere, E.; Flanders Marine Institute (VLIZ); Ghent University (UGent): Belgium; Alfred Wegener Institute for Polar and Marine Research (AWI): Germany; Leiden University: The Netherlands; (2024): Yolov8 model weights to detect unknown underwater sounds. Marine Data Archive. https://doi.org/10.14284/808
Contact:
Parcerisas, Clea Online dataset: Availability:
 This dataset is licensed under a Creative Commons Attribution 4.0 International License.Description
Machine learning for efficient segregation and labeling of potential biological sounds in long-term underwater recordings. Trained weights of model, in Python. Code to reproduce publication results, (re)train the models, or use the models for inference can be found on: GitHub - lifewatch/sound-segregation-and-categorization (https://github.com/lifewatch/sound-segregation-and-categorization). moreName
File name
Pretraining data
Hours
Scope Themes: Biology > Acoustics Keywords: Acoustic detection · Acoustics and acoustical devices, waves · Marine ecology · Underwater sound Contributors
Vlaams Instituut voor de Zee (VLIZ), more, data creator
Universiteit Gent; Faculteit Ingenieurswetenschappen en Architectuur; Departement Informatietechnologie; WAVES, more, data creator
Alfred Wegener Institute for Polar- and Marine Research; Ocean Acoustics Group (OZA), more, data creator
Universiteit Leiden; Faculteit Wetenschappen; Institute of Biology, more, data creator
Project
LifeWatch: Flemish contribution to LifeWatch.eu, more
Funding FWO International research infrastructure
Grant agreement ID I002021N
PhD: Marine Soundscapes in Shallow Water: Automated Tools for Characterization and Analysis, more
Funding Own budget, for example: patrimony, inscription fees, gifts
Grant agreement ID DOCT/003826
Publication
Describing this dataset
Parcerisas, C. et al. (2024). Machine learning for efficient segregation and labeling of potential biological sounds in long-term underwater recordings. Front. Remote Sens. 5: 1390687. https://dx.doi.org/10.3389/frsen.2024.1390687, more
Dataset status: Completed
Data type: Software/models/scripts
Data origin: Numerical calculations / models
Metadatarecord created: 2026-07-08
Information last updated: 2026-07-08
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