SleepSound : Charting the Nighttime Soundscape and Sleep Quality in Hong Kong through Machine Listening and AI-supported Information Design

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 3rd Conference on Sonification of Health and Environmental Data (SoniHED 2025)
EditorsSandra Pauletto
PublisherZenodo
Pages30-37
Number of pages8
ISBN (electronic)978-91-8106-119-2
Publication statusPublished - 29 Jan 2025

Conference

Title3rd Conference on Sonification of Health and Environmental Data
LocationKTH Royal institute of Technology
PlaceSweden
CityStockholm
Period29 January 2025

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Abstract

Sleep is an essential part of health. One factor that affects sleep quality is soundscape, the acoustic environment as perceived. In the SleepSound project, we aim to collect field data and harness machine listening for an AI-supported assessment of soundscape quality for healthy sleep. Why is this important? Our initial literature review reveals that there is little known about Hong Kong’s domestic acoustic environment and practically no research has been published on people’s perception of their own nighttime soundscape, or how it may affect sleep. A case in point is the Noise Control Ordinance (1989), which regulates noise from e.g. construction sites but leaves much of neighbourhood environments open to interpretation. Without a deeper understanding of soundscape, solving inevitable conflicts might be left to arbitrary judgements. Given this situation, our project aims to develop methods to chart the nighttime soundscape and its impact on sleep. Restorative sleep is important for everyone and crucial for vulnerable individuals e.g. with a medical condition. The present research project builds on our recent study in a nighttime hospital ward, where patients wore sleep trackers to detect disturbances, and soundscape audio was captured. SleepSound will venture further by focusing on the context of domestic bedrooms for normally healthy residents in Hong Kong.

© 2025 Lindborg et al.

Research Area(s)

  • soundscape, sleep, Hong Kong, machine learning, modeling, information design

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Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

SleepSound: Charting the Nighttime Soundscape and Sleep Quality in Hong Kong through Machine Listening and AI-supported Information Design. / Lindborg, PerMagnus; Lenzi, Sara; Li, Shirley Xin et al.
Proceedings of the 3rd Conference on Sonification of Health and Environmental Data (SoniHED 2025). ed. / Sandra Pauletto. Zenodo, 2025. p. 30-37.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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