7 citations · 9 across the 15 of their papers we have counts for
10 papers · 1 filter
Audio-to-Image Bird Species Retrieval without Audio-Image Pairs via Text Distillation
Ilyass Moummad, Marius Miron, Lukas Rauch +5
Audio-to-image retrieval offers an interpretable alternative to audio-only classification for bioacoustic species recognition, but learning aligned audio-image representations is c…
MUKA: Multi Kernel Audio Adaptation Of Audio-Language Models
Reda Bensaid, Amine Ouasfi, Yassir Bendou +4
Multimodal foundation models have demonstrated impressive generalization capabilities, yet efficiently adapting them to new tasks in a few-shot setting remains a critical challenge…
Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification
Lukas Rauch, René Heinrich, Houtan Ghaffari +4
Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A ke…
Acoustic identification of individual animals with hierarchical contrastive learning
Ines Nolasco, Ilyass Moummad, Dan Stowell +1
Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual…
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…
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning
Ilyass Moummad, Nicolas Farrugia
Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their ge…