Towards Deep Active Learning in Avian Bioacoustics

• URL: http://arxiv.org/abs/2406.18621v1
• Date: Wed, 26 Jun 2024 08:43:05 GMT
• Title: Towards Deep Active Learning in Avian Bioacoustics
• Authors: Lukas Rauch, Denis Huseljic, Moritz Wirth, Jens Decke, Bernhard Sick, Christoph Scholz,
• Abstract summary: Active learning (AL) reduces annotation cost and speed up adaptions 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.
• Score: 1.7522552085069194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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