1 citations · 2 across the 5 of their papers we have counts for
5 papers
Domain-Invariant Representation Learning of Bird Sounds
Ilyass Moummad, Romain Serizel, Emmanouil Benetos +1
Passive acoustic monitoring (PAM) is crucial for bioacoustic research, enabling non-invasive species tracking and biodiversity monitoring. Citizen science platforms provide large a…
Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
Ilyass Moummad, Nicolas Farrugia, Romain Serizel +2
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds…
Self-Supervised Learning for Few-Shot Bird Sound Classification
Ilyass Moummad, Romain Serizel, Nicolas Farrugia
Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cos…
Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning
Ilyass Moummad, Romain Serizel, Nicolas Farrugia
Deep learning has been widely used recently for sound event detection and classification. Its success is linked to the availability of sufficiently large datasets, possibly with co…
Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection
Ilyass Moummad, Romain Serizel, Nicolas Farrugia
Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio. Deep learning systems can help achieve this…