The iNaturalist Sounds Dataset
This dataset addresses the need for accessible audio data in bioacoustics, enabling research and applications for biologists, ecologists, and public engagement, though it is incremental as it builds on existing citizen science platforms.
The authors introduced the iNaturalist Sounds Dataset (iNatSounds), a collection of 230,000 audio files from over 5,500 species, and demonstrated its utility as a pretraining resource by benchmarking it on downstream evaluation datasets, showing it can improve model performance despite weak labeling.
We present the iNaturalist Sounds Dataset (iNatSounds), a collection of 230,000 audio files capturing sounds from over 5,500 species, contributed by more than 27,000 recordists worldwide. The dataset encompasses sounds from birds, mammals, insects, reptiles, and amphibians, with audio and species labels derived from observations submitted to iNaturalist, a global citizen science platform. Each recording in the dataset varies in length and includes a single species annotation. We benchmark multiple backbone architectures, comparing multiclass classification objectives with multilabel objectives. Despite weak labeling, we demonstrate that iNatSounds serves as a useful pretraining resource by benchmarking it on strongly labeled downstream evaluation datasets. The dataset is available as a single, freely accessible archive, promoting accessibility and research in this important domain. We envision models trained on this data powering next-generation public engagement applications, and assisting biologists, ecologists, and land use managers in processing large audio collections, thereby contributing to the understanding of species compositions in diverse soundscapes.