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| <center><h1 style="font-size: 225%">The iNaturalist Sounds Dataset</h1></center> | |
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| <center> | |
| <a href="https://mustafa1728.github.io" target="_blank" style="font-size: larger">Mustafa Chasmai</a> | |
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| UMass Amherst | |
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| <a href="https://www.inaturalist.org/users/44845" target="_blank" style="font-size: larger">Alexander Shepard</a> | |
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| iNaturalist | |
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| <a href="https://people.cs.umass.edu/~smaji/" target="_blank" style="font-size: larger">Subhransu Maji</a> | |
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| UMass Amherst | |
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| <a href="https://gvh.codes/" target="_blank" style="font-size: larger">Grant Van Horn</a> | |
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| UMass Amherst | |
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| <td style="font-size:20px;margin:20px;font-family:monospace"> | |
| <a style="margin:2px" href="https://openreview.net/pdf?id=QCY01LvyKm">[Paper]</a> | |
| </td> | |
| <td style="font-size:20px;margin:20px;font-family:monospace"> | |
| <a style="margin:2px" href="https://github.com/cvl-umass/iNatSounds">[Code]</a> | |
| </td> | |
| <td style="font-size:20px;margin:20px;font-family:monospace"> | |
| <a style="margin:2px" href="https://github.com/visipedia/inat_sounds">[Data]</a> | |
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| <h2>Abstract</h2> | |
| <div style="font-size:16px; text-align: justify;max-width:700px"> | |
| <p> | |
| 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 robust pretraining resource, achieving high performance relative to alternatives 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. | |
| </p> | |
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| <h2>Citation</h2> | |
| <pre style="font-size:14px; text-align: left;max-width:700px;overflow-x: scroll;background-color:beige;border-color: rgb(192, 192, 192);border-width: 2px;border-radius: 10px;border-style:solid;"> | |
| <code> | |
| @article{chasmai2024inaturalist, | |
| title={The iNaturalist Sounds Dataset}, | |
| author={Chasmai, Mustafa and Shepard, Alex and Maji, Subhransu and Van Horn, Grant}, | |
| journal={Advances in Neural Information Processing Systems}, | |
| year={2024} | |
| } | |
| </code> | |
| </pre> | |
| </body> | |
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