Mark Cartwright, Ana Elisa Mendez Mendez, Jason Cramer, Vincent Lostanlen, Graham Dove, Ho-Hsiang Wu, Justin Salamon, Oded Nov, Juan Pablo Bello
Proceedings of the International Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE)
Publication year: 2019
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.