  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.
