CIAN is the extension of the AI Chair ADSIL in Marine Bioacoustics 2020-2024 granted by AID DGA ANR. The Center of Artificial Intelligence in Natural Acoustics is a gathering of researchers with a common interest in natural bioacoustics. By understanding the subtleties of the sound orchestration that surrounds us, we can gain valuable insights into animal behavior, ecological dynamics and even contribute to conservation efforts. At the center, we use cutting-edge technologies and innovative methodologies to capture, analyze and interpret bioacoustic data.
During the last five years, we extended our researches in several connected bioacoustical research domains, all sharing our core data sciences and AI methodologies and instrumentations that we synergistically co-develop to get insight natural acoustics intelligence
LIS IM2NP MIO
Caribbean Marine Mammals Preservation Network
construction 2018-2021/extension 2023-2026
European Passive Acoustic Monitoring
LIS IMAR Akvaplan-niva Department of Earth and Environment Science, University of Pavia, Pavia The National Park of Port-Cros
Contribuer à minimiser le risque de collision entre les bateaux et de sensibiliser davantage les usagers de la mer à ce risque
construction 2018-2021/running 2022-2027
LIS ARPAL Fondation Cima LaMMA
Plateforme numérique de services intégrés pour le suivi de la biodiversité par les objets connectés et la modélisation
LIS TerrOïko SiConsult IRIT LEFE
Acoustic survey of birds, insects and bat
SYnchronized Low power Versatile Acoustic Network Including Artificial intelligence – SylvanIA
LIS MFFP IM2NP LEHNA
Le capital sol, la ressource en eau, la pression chimique, la biodiversité et l’intégrité des paysages
LIS LEFE OSUG/CNRS laboratoire Géosciences Rennes
Cochlée 3D, Intelligente et ultra basse consommation – ULP-COCHLEA
LIS CRIStAL INPS IEMN
In our laboratory, we explore the intersection of acoustics and biology, presenting a multidisciplinary study focused on the detection, localization, and classification of animal sounds. Our advanced acoustics techniques, coupled with biological investigations, offer a nuanced understanding of various species’ behaviors. Employing cutting-edge acoustic detection methods and localization techniques, we try to precisely capture and pinpoint animal vocalizations.
By integrating machine learning for classification, we distinguish between different signals, revealing not only the acoustic intricacies but also correlating them with biological findings. This holistic approach provides a richer perspective on animal behaviors, contributing valuable insights to both acoustics and biology. We are eager to share our findings across diverse journals, fostering collaboration and advancing the collective knowledge of the intricate world of animal behaviors.