Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Guimaraes, H. R.; Abdollahi, M.; Zhu, Y.; Maucourt, S.; Coallier, N.; Giovenazzo, P.; Falk, T. H.
Benchmarking Self-Supervised Audio Representations for IoT-Enabled Acoustic Beehive Monitoring Article de journal
Dans: IEEE Internet of Things Journal, vol. 12, no 21, p. 45000–45010, 2025, ISSN: 23274662 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Acoustics, Audio acoustics, Audio representation, Beehive monitoring, Benchmarking, Bioacoustics, Computer vision applications, Deep learning, Honeybee, honeybees, Internet of Things (IoT), IoT, Labeled data, Performance, Real time systems, Self-supervised learning, self-supervised learning (SSL), Societal benefits, Speech applications, Speech recognition, Supervised learning, Universal feature extractors
@article{guimaraesBenchmarkingSelfSupervisedAudio2025,
title = {Benchmarking Self-Supervised Audio Representations for IoT-Enabled Acoustic Beehive Monitoring},
author = {H. R. Guimaraes and M. Abdollahi and Y. Zhu and S. Maucourt and N. Coallier and P. Giovenazzo and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105013592730?origin=resultslist},
doi = {10.1109/JIOT.2025.3599483},
issn = {23274662 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Internet of Things Journal},
volume = {12},
number = {21},
pages = {45000–45010},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Self-supervised learning (SSL) has enabled the development of universal feature extractors that have redefined the performance envelope of computer vision and speech applications. Recent works have started to explore SSL in other domains, including bioacoustics, which could have significant societal benefits. Honeybees (Apis mellifera), for example, are crucial pollinators contributing to one-third of global food production. However, massive colony losses in recent years have raised concerns. Traditional hive monitoring methods rely on intrusive visual inspections by beekeepers, which can further disrupt colony dynamics. As such, Internet of Things (IoT)-based automated monitoring systems have emerged, integrating environmental and bioacoustic sensing to enable real-time, noninvasive hive assessment. In this work, we introduce a comprehensive evaluation and benchmarking of general-purpose and bioacoustic audio representations that generalize across various tasks in IoT-enabled acoustic beehive monitoring, even with limited labeled data. Herein, fourteen models are evaluated across four critical tasks: beehive state detection, beehive strength assessment, buzzing identification, and beekeeper voice activity detection. Reported results demonstrate the strong generalizability of existing representations, paving the way for advanced, scalable honeybee colony monitoring and preservation. © 2014 IEEE.},
keywords = {Acoustics, Audio acoustics, Audio representation, Beehive monitoring, Benchmarking, Bioacoustics, Computer vision applications, Deep learning, Honeybee, honeybees, Internet of Things (IoT), IoT, Labeled data, Performance, Real time systems, Self-supervised learning, self-supervised learning (SSL), Societal benefits, Speech applications, Speech recognition, Supervised learning, Universal feature extractors},
pubstate = {published},
tppubtype = {article}
}
Self-supervised learning (SSL) has enabled the development of universal feature extractors that have redefined the performance envelope of computer vision and speech applications. Recent works have started to explore SSL in other domains, including bioacoustics, which could have significant societal benefits. Honeybees (Apis mellifera), for example, are crucial pollinators contributing to one-third of global food production. However, massive colony losses in recent years have raised concerns. Traditional hive monitoring methods rely on intrusive visual inspections by beekeepers, which can further disrupt colony dynamics. As such, Internet of Things (IoT)-based automated monitoring systems have emerged, integrating environmental and bioacoustic sensing to enable real-time, noninvasive hive assessment. In this work, we introduce a comprehensive evaluation and benchmarking of general-purpose and bioacoustic audio representations that generalize across various tasks in IoT-enabled acoustic beehive monitoring, even with limited labeled data. Herein, fourteen models are evaluated across four critical tasks: beehive state detection, beehive strength assessment, buzzing identification, and beekeeper voice activity detection. Reported results demonstrate the strong generalizability of existing representations, paving the way for advanced, scalable honeybee colony monitoring and preservation. © 2014 IEEE.