Passive acoustic monitoring (PAM) in avian bioacoustics enables cost-effective and extensive data collection with minimal disruption to natural habitats. Despite advancements in computational avian bioacoustics, deep learning models continue to encounter challenges in adapting to diverse environments in practical PAM scenarios. This is primarily due to the scarcity of annotations, which requires labor-intensive efforts from human experts. Active learning (AL) reduces annotation cost and speed ups adaption to diverse scenarios by querying the most informative instances for labeling. This paper outlines a deep AL approach, introduces key challenges, and conducts a small-scale pilot study.

翻译：鸟类生物声学中的被动声学监测（PAM）能够以经济高效的方式进行广泛的数据收集，同时对自然栖息地的干扰最小。尽管计算鸟类生物声学领域取得了进展，深度学习模型在实际PAM场景中适应多样化环境方面仍面临挑战。这主要是由于标注数据的稀缺，需要人类专家投入大量劳动。主动学习（AL）通过查询信息量最大的实例进行标注，降低了标注成本，并加速了对多样化场景的适应。本文概述了一种深度AL方法，介绍了关键挑战，并开展了一项小规模试点研究。