Optimal pretraining strategies for few-shot bioacoustic transfer learning

Determine the optimal pretraining strategies to produce pretrained networks that enable accurate few-shot transfer learning under substantial domain shift and limited annotated data in novel bioacoustic domains.

Background

The paper targets transfer learning for passive acoustic monitoring in data-deficient marine environments, especially coral reefs, where annotated datasets are scarce and domain shift from terrestrial datasets is substantial.

While pretrained models exist for birds and general audio, the authors note that crafting novel pretrained networks tailored for few-shot learning in new bioacoustic domains is hampered by uncertainty about which pretraining strategies are best, particularly given limited labeled data.