  Self-supervised learning (SSL) in audio holds significant potential across
various domains, particularly in situations where abundant, unlabeled data is
readily available at no cost. This is pertinent in bioacoustics, where
biologists routinely collect extensive sound datasets from the natural
environment. In this study, we demonstrate that SSL is capable of acquiring
meaningful representations of bird sounds from audio recordings without the
need for annotations. Our experiments showcase that these learned
representations exhibit the capacity to generalize to new bird species in
few-shot learning (FSL) scenarios. Additionally, we show that selecting windows
with high bird activation for self-supervised learning, using a pretrained
audio neural network, significantly enhances the quality of the learned
representations.
