From the 1 of 5 linked papers with an AI index.
5 papers
MetaPerch: Learning from metadata for bioacoustics foundation models
Mustafa Chasmai, Vincent Dumoulin, Jenny Hamer
The paper presents MetaPerch, a bioacoustic foundation model that uses recording metadata (e.g., location, time) as auxiliary supervision to improve species identification performa…
Capturing Individual Human Preferences with Reward Features
André Barreto, Vincent Dumoulin, Yiran Mao +6
Reinforcement learning from human feedback usually models preferences using a reward function that does not distinguish between people. We argue that this is unlikely to be a good…
Perch 2.0: The Bittern Lesson for Bioacoustics
Bart van Merriënboer, Vincent Dumoulin, Jenny Hamer +3
Perch is a performant pre-trained model for bioacoustics. It was trained in supervised fashion, providing both off-the-shelf classification scores for thousands of vocalizing speci…
Perch 2.0 transfers 'whale' to underwater tasks
Andrea Burns, Lauren Harrell, Bart van Merriënboer +3
Perch 2.0 is a supervised bioacoustics foundation model pretrained on 14,597 species, including birds, mammals, amphibians, and insects, and has state-of-the-art performance on mul…
The Search for Squawk: Agile Modeling in Bioacoustics
Vincent Dumoulin, Otilia Stretcu, Jenny Hamer +16
Passive acoustic monitoring (PAM) has shown great promise in helping ecologists understand the health of animal populations and ecosystems. However, extracting insights from millio…