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Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors
Kather,Vincent S. Ghani,Burooj Stowell,Dan
Kather,Vincent S.
Ghani,Burooj
Stowell,Dan
Abstract
In computational bioacoustics, deep learning models are composed of feature extractors and classifiers. The feature extractors generate vector representations of the input sound segments, called embeddings, which can be input to a classifier. While benchmarking of classification scores provides insights into specific performance statistics, it is limited to species that are included in the models' training data. Furthermore, it makes it impossible to compare models trained on very different taxonomic groups. This paper aims to address this gap by analyzing the embeddings generated by the feature extractors of 15 bioacoustic models spanning a wide range of setups (model architectures, training data, training paradigms). We evaluated and compared different ways in which models structure embedding spaces through clustering and kNN classification, which allows us to focus our comparison on feature extractors independent of their classifiers. We believe that this approach lets us evaluate the adaptability and generalization potential of models going beyond the classes they were trained on.
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conference
Date
2025-04-09
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2504.06710v1.pdf
Adobe PDF, 1.98 MB
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cs.LG
Citation
Kather, V S, Ghani, B & Stowell, D 2025, Clustering and novel class recognition : evaluating bioacoustic deep learning feature extractors. in Proceedings of the 11th Convention of the European Acoustics Association : Forum Acusticum EuroNoise 2025. pp. 2153-2160, Forum Acusticum EuroNoise 2025, Malaga, Spain, 23/06/25.
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info:eu-repo/semantics/openAccess