Exploring emergent soundscape profiles from crowdsourced audio data

Abstract

The key component of designing sustainable, enriching, and inclusive cities is public participation. The soundscape is an integral part of an immersive environment in cities, and it should be considered as a resource that creates the acoustic image for an urban environment. For urban planning professionals, this requires an understanding of the constituents of citizens' emergent soundscape experience. The goal of this study is to present a systematic method for analyzing crowdsensed soundscape data with unsupervised machine learning methods. This study applies a crowdsensed sound- scape experience data collection method with low threshold for participation. The aim is to analyze the data using unsupervised machine learning methods to give insights into soundscape perception and quality.

For this purpose, qualitative and raw audio data were collected from 111 participants in Helsinki, Finland, and then clustered and further analyzed. We conclude that a machine learning analysis combined with accessible, mobile crowdsensing methods enable results that can be applied to track hidden experiential phenomena in the urban soundscape.