  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.
