Capturing the Sounds of an Urban Greenspace

Abstract

<p><strong>Abstract.</strong> Acoustic data can be a source of important information about events and the environment in modern cities. To date, much of the focus has been on monitoring noise pollution, but the urban soundscape contains a rich variety of signals about both human and natural phenomena. We describe the CitySounds project, which has installed enclosed sensor kits at several locations across a heavily used urban greenspace in the city of Edinburgh. The acoustic monitoring components regularly capture short clips in real-time of both ultrasonic and audible noises, for example encompassing bats, birds and other wildlife, traffic, and human. The sounds are complemented by collecting other data from sensors, such as temperature and relative humidity. To ensure privacy and compliance with relevant legislation, robust methods render completely unintelligible any traces of voice or conversation that may incidentally be overheard by the sensors. We have adopted a variety of methods to encourage community engagement with the audio data and to communicate the richness of urban soundscapes to a general audience.</p>

Acoustic Characterization of Potential Quiet Areas in Dortmund, Germany

“…To date, there are few, if any, comparable studies to refer or relate to the BPI findings, emphasizing the need for continued study of quiet areas across broad urban regions. This also implies that the unique land use stratified automated aural sampling procedures [63] of temporally dense big data sets are a unique geographic sampling approach that can deliver a higher resolution understanding of the spatio-temporal urban acoustic environment not possible in studies with limited sample sizes [18,29,36,41,72,83,[90][91][92][93][94][95].…”

Acoustic Characterization of Potential Quiet Areas in Dortmund, Germany

“…To date, there are few if any comparable studies to refer or relate the BPI findings with, emphasizing the need for continued study of quiet areas across broad urban regions. This also implies that the unique land use stratified automated aural sampling procedures [63] of temporally dense big data sets can deliver higher resolution understanding of the spatio-temporal urban acoustic environment not possible in studies with limited sample sizes [18,29,36,41,72,83,[90][91][92][93][94][95].…”