SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network

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

• Publication year

  2019

• Venue

  Workshop on Detection and Classification of Acoustic Scenes and Events

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.33682/j5zw-2t88
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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