  Bioacoustic sound event detection allows for better understanding of animal
behavior and for better monitoring biodiversity using audio. Deep learning
systems can help achieve this goal, however it is difficult to acquire
sufficient annotated data to train these systems from scratch. To address this
limitation, the Detection and Classification of Acoustic Scenes and Events
(DCASE) community has recasted the problem within the framework of few-shot
learning and organize an annual challenge for learning to detect animal sounds
from only five annotated examples. In this work, we regularize supervised
contrastive pre-training to learn features that can transfer well on new target
tasks with animal sounds unseen during training, achieving a high F-score of
61.52%(0.48) when no feature adaptation is applied, and an F-score of
68.19%(0.75) when we further adapt the learned features for each new target
task. This work aims to lower the entry bar to few-shot bioacoustic sound event
detection by proposing a simple and yet effective framework for this task, by
also providing open-source code.
