Détail du document
Identifiant
oai:arXiv.org:2406.18621
Sujet
Computer Science - Sound Computer Science - Artificial Inte... Electrical Engineering and Systems...Auteur
Rauch, Lukas Huseljic, Denis Wirth, Moritz Decke, Jens Sick, Bernhard Scholz, ChristophCatégorie
Computer Science
Année
2024
Date de référencement
13/11/2024
Mots clés
learning bioacoustics avian science deepRésumé
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
