TL;DR
This study introduces a participant-led method using weighted k-means clustering to identify characteristic soundscapes in Singapore, ensuring ecological validity and reducing selection bias in soundscape research.
Contribution
It presents a novel participant-driven approach for selecting representative soundscapes based on perceptual attributes, validated through clustering of locations across Singapore.
Findings
Identified 62 locations with characteristic soundscapes in Singapore.
Validated the method using the ISO 12913-2 soundscape perception model.
Enhanced reliability of soundscape selection through participant confidence weights.
Abstract
The ecological validity of soundscape studies usually rests on a choice of soundscapes that are representative of the perceptual space under investigation. For example, a soundscape pleasantness study might investigate locations with soundscapes ranging from "pleasant" to "annoying". The choice of soundscapes is typically researcher-led, but a participant-led process can reduce selection bias and improve result reliability. Hence, we propose a robust participant-led method to pinpoint characteristic soundscapes possessing arbitrary perceptual attributes. We validate our method by identifying Singaporean soundscapes spanning the perceptual quadrants generated from the "Pleasantness" and "Eventfulness" axes of the ISO 12913-2 circumplex model of soundscape perception, as perceived by local experts. From memory and experience, 67 participants first selected locations corresponding to each…
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Taxonomy
Methodsk-Means Clustering
