Determinantal point process sampling for bioacoustic active learning
Eco-acoustic monitoring generates vast volumes of audio data, making active learning a promising approach for reducing annotation effort while efficiently training reliable biodiversity classifiers. This report presents CARE-DPP, a batch active-learning acquisition method submitted to BioDCASE Active Learning for Bioacoustics 2026 challenge. The method combines class-balanced predictive uncertainty with embedding-space novelty, while a determinantal point process (DPP) objective selects a high-quality and non-redundant acquisition batch. The uncertainty-novelty balance is annealed over the annotation budget: early cycles emphasize geometric coverage, whereas later cycles increasingly expl...
| Authors | Hugo Magaldi, Gabriel Dubus |
|---|---|
| Primary category | cs.SD |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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