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

Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)

Published October 25, 2019

Mark Cartwright, Ana Elisa Mendez Mendez, Jason Cramer, Vincent Lostanlen, Graham Dove, Ho-Hsiang Wu, Justin Salamon, Oded Nov, Juan Pablo Bello

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.

Learn More

Research Areas:  AI & Machine Learning Audio