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Rapport Année : 2023

Supervised contrastive learning for pre-training bioacoustic few-shot systems

Résumé

We show in this work that learning a rich feature extractor from scratch using only official training data is feasible. We achieve this by learning representations using a supervised contrastive learning framework. We then transfer the learned feature extractor to the sets of validation and test for few-shot evaluation. For fewshot validation, we simply train a linear classifier on the negative and positive shots and obtain a F-score of 63.46% outperforming the baseline by a large margin. We don't use any external data or pretrained model. Our approach doesn't require choosing a threshold for prediction or any post-processing technique. Our code is publicly available on Github : https://github.com/ ilyassmoummad/dcase23_task5_scl
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Dates et versions

hal-04165306 , version 1 (25-07-2023)

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Paternité

Identifiants

  • HAL Id : hal-04165306 , version 1

Citer

Ilyass Moummad, Romain Serizel, Nicolas Farrugia. Supervised contrastive learning for pre-training bioacoustic few-shot systems. IMT Atlantique; LORIA. 2023. ⟨hal-04165306⟩
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