Categorizing Shallow Marine Soundscapes Using Explained Clusters

Natural marine soundscapes are being threatened by increasing anthropic noise, particularly in shallow coastal waters. To preserve and monitor these soundscapes, understanding them is essential. Here, we propose a new method for semi-supervised categorization of shallow marine soundscapes, with further interpretation of these categories according to concurrent environmental conditions. The proposed methodology uses a nonlinear mapping of short-term spectrograms to a two-dimensional space, followed by a density-based clustering algorithm to identify similar sound environments. A random forest classifier, based on additional environmental data, is used to predict their occurrence. Finally, explainable machine learning tools provide insight into the ecological explanation of the clusters. This methodology was tested in the Belgian part of the North Sea, and resulted in clearly identifiable categories of soundscapes that could be explained by spatial and temporal environmental parameters, such as distance to the shore, bathymetry, tide or season. Classifying soundscapes facilitates their identification, which can be useful for policy making or conservation programs. Soundscape categorization, as proposed in this work, could be used to monitor acoustic trends and patterns in space and time that might provide useful indicators of biodiversity and ecosystem functionality change.

Automated summary

To protect and monitor marine soundscapes, a new method for categorization and interpretation of shallow marine soundscapes is proposed. The method involves mapping spectrograms to a two-dimensional space, clustering similar sound environments, using a random forest classifier to predict the occurrence, and using explainable machine learning tools for ecological explanation. The methodology was tested in the Belgian part of the North Sea, resulting in identifiable soundscape categories explained by environmental parameters. Soundscape categorization can be used for policy making or conservation programs, and as indicators of biodiversity and ecosystem functionality change.