Dokumentdetails
ID
oai:arXiv.org:2406.18621
Thema
Computer Science - Sound Computer Science - Artificial Inte... Electrical Engineering and Systems...Autor
Rauch, Lukas Huseljic, Denis Wirth, Moritz Decke, Jens Sick, Bernhard Scholz, ChristophKategorie
Computer Science
Jahr
2024
Auflistungsdatum
13.11.2024
Schlüsselwörter
learning bioacoustics avian science deepZusammenfassung
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.
;Comment: accepted at IAL@ECML-PKDD24
Rauch, Lukas,Huseljic, Denis,Wirth, Moritz,Decke, Jens,Sick, Bernhard,Scholz, Christoph, 2024, Towards Deep Active Learning in Avian Bioacoustics
