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\begin{document}

\title{Deep learning for passive acoustic monitoring: how to study changing
phenology in remote areas}



\author[1]{Sylvain Christin}%
\author[2]{Éric Hervet}%
\author[3]{Paul Smith}%
\author[4]{Ray Alisauskas}%
\author[5]{Dominique Berteaux}%
\author[6]{Glen Brown}%
\author[7]{Kyle Elliott}%
\author[8]{Jannik Hansen}%
\author[5]{Sandra Lai}%
\author[9]{Jean-François Lamarre}%
\author[10]{Richard Lanctot}%
\author[11]{Christopher Latty}%
\author[12]{Audrey Le Pogam}%
\author[3]{Douglas MacNearney}%
\author[13]{Vijay Patil}%
\author[14]{Jennie Rausch}%
\author[15]{Sarah Saalfeld}%
\author[8]{Niels Schmidt}%
\author[5]{Andrew Tam}%
\author[5]{François Vézina}%
\author[16]{Øystein Varpe}%
\author[14]{Paul Woodard}%
\author[17]{Glenn Yannic}%
\author[18]{Nicolas Lecomte}%
\affil[1]{University of Moncton}%
\affil[2]{Université de Moncton}%
\affil[3]{Environment and Climate Change Canada National Wildlife Research Centre}%
\affil[4]{Environment and Climate Change Canada}%
\affil[5]{Universite du Quebec a Rimouski}%
\affil[6]{Ontario Ministry of Natural Resources}%
\affil[7]{McGill University}%
\affil[8]{Aarhus Universitet}%
\affil[9]{Polar Knowledge Canada}%
\affil[10]{US Fish and Wildlife Service}%
\affil[11]{National Wildlife Refuge}%
\affil[12]{Université du Québec à Rimouski}%
\affil[13]{US Geological Survey Alaska Region}%
\affil[14]{Environment and Climate Change Canada Canadian Wildlife Service}%
\affil[15]{US Fish and Wildlife Service Alaska Region}%
\affil[16]{University of Bergen}%
\affil[17]{Universite Grenoble Alpes}%
\affil[18]{Universite de Moncton}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\selectlanguage{english}
\begin{abstract}
Understanding how species adjust to seasonality is fundamental in
ecology, especially with rapidly increasing global air temperatures.
Bioacoustic monitoring offers promise for tracking shifts in seasonal
timing of vocal species, as recent automated sound recorders enable
large-scale and long-term data collection. Yet, analyzing vast datasets
necessitates automation and innovative detection methods. Here, we
introduce BioSoundNet, a deep learning model designed for bird
vocalization detection. Trained on field data and open-access databases,
BioSoundNet achieved AUC scores of 0.88-0.93 and average precisions of
0.87-0.97 across five datasets spanning various ecosystems, and
effectively captured the temporal patterns of avian acoustic activity at
different time scales. Our findings underline the importance of
evaluating models in ecological contexts and to address the potential
consequences of missing detections. Operating efficiently on standard
computers, BioSoundNet is a robust tool for automated bird vocalization
detection, providing a valuable resource for ecological phenology
studies and acoustic dataset analysis.%
\end{abstract}%



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