SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context

• URL: http://arxiv.org/abs/2009.05188v1
• Date: Fri, 11 Sep 2020 01:19:12 GMT
• Title: SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
• Authors: Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez, Yu Wang, Ho-Hsiang Wu, Vincent Lostanlen, Magdalena Fuentes, Graham Dove, Charlie Mydlarz, Justin Salamon, Oded Nov, and Juan Pablo Bello
• Abstract summary: We present a dataset for urban sound tagging withtemporal information. This dataset provides the opportunity to investigate how metadata can aid in the prediction of the urban sound tags.
• Score: 32.84541094143274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems for real-world urban noise monitoring. While datasets of urban recordings are available, this dataset provides the opportunity to investigate how spatiotemporal metadata can aid in the prediction of urban sound tags. SONYC-UST-V2 consists of 18510 audio recordings from the "Sounds of New York City" (SONYC) acoustic sensor network, including the timestamp of audio acquisition and location of the sensor. The dataset contains annotations by volunteers from the Zooniverse citizen science platform, as well as a two-stage verification with our team. In this article, we describe our data collection procedure and propose evaluation metrics for multilabel classification of urban sound tags. We report the results of a simple baseline model that exploits spatiotemporal information.

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