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 particularly 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.

翻译：自监督学习（SSL）在音频领域具有巨大潜力，尤其在大量无标签数据可免费获取的场景中表现突出。这一点在生物声学中尤为重要——生物学家通常会从自然环境中系统收集海量声音数据集。在本研究中，我们证明自监督学习能够从无需标注的音频记录中获取有意义的鸟类声音表征。实验表明，这些习得的表征具备在少样本学习（FSL）场景中泛化至新鸟种的能力。此外，我们证明通过使用预训练音频神经网络筛选高鸟类活动强度的时窗进行自监督学习，能显著提升学习表征的质量。