AVB81: A Fine-Grained Audiovisual Dataset for Bird Classification in the Wild

Automated bird monitoring plays a crucial role in ecoinformatics and biodiversity conservation. Despite significant advancements in fine-grained visual classification, targets in complex wild environments frequently encounter interferences such as long capture distances, severe visual occlusion, and strong background noise, rendering single-modal perception highly susceptible to performance bottlenecks. To advance research in audiovisual multimodal bird classification, this paper introduces AVB81, a multimodal dataset tailored for fine-grained bird recognition. Covering 81 bird species in North America, the dataset comprises 3,247 fixed-length 10-second field videos, me-ticulously supplemented with 5,418 independent audios and 7,083 high-quality static images.