| Title: | SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network |
| Authors: | Cartwright, Mark Mendez, Ana Elisa Mendez Cramer, Aurora Lostanlen, Vincent Dove, Graham Wu, Ho-Hsiang Salamon, Justin Nov, Oded Bello, Juan |
| Date Issued: | Oct-2019 |
| Citation: | M. Cartwright, A. Mendez, A. Cramer, V. Lostanlen, G. Dove, H. Wu, J. Salamon, O. Nov & J. Bello, "SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network", Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019), pages 35–39, New York University, NY, USA, Oct. 2019 |
| Abstract: | SONYC Urban Sound Tagging (SONYC-UST) is a dataset for the development and evaluation of machine listening systems for real-world urban noise monitoring. It consists of 3068 audio recordings from the "Sounds of New York City" (SONYC) acoustic sensor network. Via the Zooniverse citizen science platform, volunteers tagged the presence of 23 fine-grained classes that were chosen in consultation with the New York City Department of Environmental Protection. These 23 fine-grained classes can be grouped into eight coarse-grained classes. In this work, we describe the collection of this dataset, metrics used to evaluate tagging systems, and the results of a simple baseline model. |
| First Page: | 35 |
| Last Page: | 39 |
| DOI: | https://doi.org/10.33682/j5zw-2t88 |
| Type: | Article |
| Appears in Collections: | Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019) |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| DCASE2019Workshop_Cartwright_4.pdf | 910.72 kB | Adobe PDF | View/Open |
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