[2603.22053] AnimalCLAP: Taxonomy-Aware Language-Audio Pretraining for Species Recognition and Trait Inference

Computer Science > Sound arXiv:2603.22053 (cs) [Submitted on 23 Mar 2026] Title:AnimalCLAP: Taxonomy-Aware Language-Audio Pretraining for Species Recognition and Trait Inference Authors:Risa Shinoda, Kaede Shiohara, Nakamasa Inoue, Hiroaki Santo, Fumio Okura View a PDF of the paper titled AnimalCLAP: Taxonomy-Aware Language-Audio Pretraining for Species Recognition and Trait Inference, by Risa Shinoda and 4 other authors View PDF HTML (experimental) Abstract:Animal vocalizations provide crucial insights for wildlife assessment, particularly in complex environments such as forests, aiding species identification and ecological monitoring. Recent advances in deep learning have enabled automatic species classification from their vocalizations. However, classifying species unseen during training remains challenging. To address this limitation, we introduce AnimalCLAP, a taxonomy-aware language-audio framework comprising a new dataset and model that incorporate hierarchical biological information. Specifically, our vocalization dataset consists of 4,225 hours of recordings covering 6,823 species, annotated with 22 ecological traits. The AnimalCLAP model is trained on this dataset to align audio and textual representations using taxonomic structures, improving the recognition of unseen species. We demonstrate that our proposed model effectively infers ecological and biological attributes of species directly from their vocalizations, achieving superior performance compared to CLAP...