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  5. SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network
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dc.contributor.authorCartwright, Mark-
dc.contributor.authorMendez, Ana Elisa Mendez-
dc.contributor.authorCramer, Aurora-
dc.contributor.authorLostanlen, Vincent-
dc.contributor.authorDove, Graham-
dc.contributor.authorWu, Ho-Hsiang-
dc.contributor.authorSalamon, Justin-
dc.contributor.authorNov, Oded-
dc.contributor.authorBello, Juan-
dc.date.accessioned2019-10-24T01:50:25Z-
dc.date.available2019-10-24T01:50:25Z-
dc.date.issued2019-10-
dc.identifier.citationM. 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. 2019en
dc.identifier.urihttp://hdl.handle.net/2451/60776-
dc.description.abstractSONYC 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.en
dc.rightsDistributed under the terms of the Creative Commons Attribution 4.0 International (CC-BY) license.en
dc.titleSONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Networken
dc.typeArticleen
dc.identifier.DOIhttps://doi.org/10.33682/j5zw-2t88en
dc.description.firstPage35en
dc.description.lastPage39en
Appears in Collections:Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)

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