• Céline Angonin

    Tilburg University

    Multi-task deep learning methods applied to bioacoustic tasks

  • Ilaria Morandi

    University of South Bohemia

    Bioacoustics for the conservation of species affected by climate change.

  • Juan Sebastián Cañas

    University College London

    Artificial Intelligence for Biodiversity Conservation and Climate Change

  • Manuel Alejandro Jaramillo Rodriguez

    KU Leuven

    Machine learning and deep learning algorithms for acoustic signal processing

  • Marius Faiß

    Max Planck Institute of Animal Behavior

    Vocal interactions in social mammals, adaptive audio representations for machine-learning

  • Minkyung Kwak

    Charles University

    Animal communication, song evolution, bio/ecoacoustic, conservation ecology

  • Roberto Alessandri

    Sorama

    AI-enhanced sound event localization and detection for Bioacoustic

  • Shiqi Zhang

    Tampere University

    Acoustic Signal Processing (enhancement / separation / detection / classification / recognition)

  • Vincent Kather

    Naturalis Biodiversity Center

    Biodiversity monitoring through bioacoustics using new deep learning methods

  • Yasmine Benhamadi

    Centre National de la Recherche Scientifique (CNRS)

    Machine Learning, Embedded Computing, Batteryless Computing, Computational Bioacoustics, Sustainability.