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Clustering, categorizing, and mapping of shallow coastal water soundscapes
Parcerisas, C.; Botteldooren, D.; Devos, P.; Debusschere, E. (2023). Clustering, categorizing, and mapping of shallow coastal water soundscapes, in: Forum Acusticum 2023: 10th Convention of the European Acoustics Association, Turin, Italy, 11th – 15th September 2023. pp. 6091-6097. https://dx.doi.org/10.61782/fa.2023.1070
In: (2023). Forum Acusticum 2023: 10th Convention of the European Acoustics Association, Turin, Italy, 11th – 15th September 2023. Italian Acoustical Association: Torino.
Related to:
Calonge, A.; Develter, R.; Muñiz, C.; Parcerisas, C.; Reubens, J.; Boone, W.; Deneudt, K.; Debusschere, E. (2026). Scalable low-cost seabed landers: The missing link for sustained, integrated, long-term observations in dynamic shallow seas. Remote Sensing in Ecology and Conservation Online first: 1-9. https://dx.doi.org/10.1002/rse2.70072, more
Available in  Authors 
    Vlaams Instituut voor de Zee: Open access 395202 [ download pdf ]
Document type: Conference paper
Keywords
    Underwater acoustics
    Water > Shallow water
    Marine/Coastal
Author keywords
    soundscape analysis, unsupervised categorization, shap
Project Top | Authors 
  • Marine Soundscapes in Shallow Water: Automated Tools for Characterization and Analysis
Authors  Top 
  • Parcerisas, C.
  • Botteldooren, D.
  • Devos, P.
  • Debusschere, E.
Abstract
    For many of its inhabitants, the underwater soundscape is a rich source of information that may be crucial for their survival. Moreover, in shallow coastal waters where visibility is poor, the importance of sound is emphasized. Yet coastal waters are also rich in anthropogenic sounds which may disturb the ecosystem. Passive Acoustics Monitoring (PAM) is a flexible, non-invasive, and cost-effective solution to acquire information at habitat or community level. Studying the acoustic scene of a habitat in a global, holistic way is known as soundscape analysis. How-ever, there are currently no standardized methods to characterize and understand marine soundscapes in an automated way. Here we propose a methodology for clustering underwater soundscapes and linking the obtained categories to environmental parameters in space and time.This is done using explainable artificial intelligence. The methodology is applied to a PAM dataset collected in the Belgian Part of the North Sea. The obtained categories focus on background sound, which includes all combinations of sounds that occur under certain conditions at specific places. With this information, the marine acoustic scene and its change over space and time can be mapped for the whole area of interest.
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