Domain-Invariant Representation Learning of Bird Sounds

Ilyass Moummad (1, 2) , Romain Serizel (3) , Emmanouil Benetos (4, 5) , Nicolas Farrugia (1, 2)

1 Lab_STICC_BRAIn - Equipe Better Representations for Artificial Intelligence
2 IMT Atlantique - MEE - Département Mathematical and Electrical Engineering
3 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
4 Centre for Digital Music
5 QMUL - Queen Mary University of London

Passive acoustic monitoring (PAM) is crucial for bioacoustic research, enabling non-invasive species tracking and biodiversity monitoring. Citizen science platforms like Xeno-Canto provide large annotated datasets from focal recordings, where the target species is intentionally recorded. However, PAM requires monitoring in passive soundscapes, creating a domain shift between focal and passive recordings, which challenges deep learning models trained on focal recordings. To address this, we leverage supervised contrastive learning to improve domain generalization in bird sound classification, enforcing domain invariance across same-class examples from different domains. We also propose ProtoCLR (Prototypical Contrastive Learning of Representations), which reduces the computational complexity of the SupCon loss by comparing examples to class prototypes instead of pairwise comparisons. Additionally, we present a new few-shot classification benchmark based on BirdSet, a large-scale bird sound dataset, and demonstrate the effectiveness of our approach in achieving strong transfer performance.

Mots clés: Supervised Contrastive Learning; Domain Generalization; Few-shot Learning; Bioacoustics

Domaines: Son [cs.SD]

Ilyass Moummad, Romain Serizel, Emmanouil Benetos, Nicolas Farrugia. Domain-Invariant Representation Learning of Bird Sounds. 2024. ⟨hal-04696391⟩