  Noise is one of the primary quality-of-life issues in urban environments. In
addition to annoyance, noise negatively impacts public health and educational
performance. While low-cost sensors can be deployed to monitor ambient noise
levels at high temporal resolutions, the amount of data they produce and the
complexity of these data pose significant analytical challenges. One way to
address these challenges is through machine listening techniques, which are
used to extract features in attempts to classify the source of noise and
understand temporal patterns of a city's noise situation. However, the
overwhelming number of noise sources in the urban environment and the scarcity
of labeled data makes it nearly impossible to create classification models with
large enough vocabularies that capture the true dynamism of urban soundscapes
In this paper, we first identify a set of requirements in the yet unexplored
domain of urban soundscape exploration. To satisfy the requirements and tackle
the identified challenges, we propose Urban Rhapsody, a framework that combines
state-of-the-art audio representation, machine learning, and visual analytics
to allow users to interactively create classification models, understand noise
patterns of a city, and quickly retrieve and label audio excerpts in order to
create a large high-precision annotated database of urban sound recordings. We
demonstrate the tool's utility through case studies performed by domain experts
using data generated over the five-year deployment of a one-of-a-kind sensor
network in New York City.
