<ul><li>A paper in arXiv discusses the evaluation of bioacoustic deep learning feature extractors for clustering and novel class recognition.</li><li>The research aims to address the limitation of benchmarking classification scores, which is specific to the training data and does not allow comparison across different taxonomic groups.</li><li>The study analyzes the embeddings generated by 15 bioacoustic models to evaluate their adaptability and generalization potential.</li><li>Clustering and kNN classification are used to structure the embedding spaces, allowing comparison of feature extractors independent of their classifiers.</li></ul>

Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors

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