Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTan, Ee-Lengen_US
dc.contributor.authorKarnapi, Furi Andien_US
dc.contributor.authorNg, Linus Junjiaen_US
dc.contributor.authorOoi, Kennethen_US
dc.contributor.authorGan, Woon-Sengen_US
dc.identifier.citationTan, E., Karnapi, F. A., Ng, L. J., Ooi, K. & Gan, W. (2021). Extracting urban sound information for residential areas in smart cities using an end-to-end IoT system. IEEE Internet of Things Journal, 8(18), 14308-14321.
dc.description.abstractWith rapid urbanization comes the increase of community, construction, and transportation noise in residential areas. The conventional approach of solely relying on sound pressure level (SPL) information to decide on the noise environment and to plan out noise control and mitigation strategies is inadequate. This paper presents an end-to-end IoT system that extracts real-time urban sound metadata using edge devices, providing information on the sound type, location and duration, rate of occurrence, loudness, and azimuth of a dominant noise in nine residential areas. The collected metadata on environmental sound is transmitted to and aggregated in a cloud-based platform to produce detailed descriptive analytics and visualization. Our approach in integrating different building blocks, namely, hardware, software, cloud technologies, and signal processing algorithms to form our real-time IoT system is outlined. We demonstrate how some of the sound metadata extracted by our system are used to provide insights into the noise in residential areas. A scalable workflow to collect and prepare audio recordings from nine residential areas to construct our urban sound dataset for training and evaluating a location-agnostic model is discussed. Some practical challenges of managing and maintain a sensor network deployed at numerous locations are also addressed.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation.ispartofIEEE Internet of Things Journalen_US
dc.rights© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleExtracting urban sound information for residential areas in smart cities using an end-to-end IoT systemen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.researchCentre for Information Sciences and Systemsen_US
dc.description.versionAccepted versionen_US
dc.subject.keywordsAcoustic Source Event Detectionen_US
dc.subject.keywordsDeep Neural Networksen_US
dc.subject.keywordsEdge Analyticsen_US
dc.subject.keywordsEdge-Cloud Architectureen_US
dc.subject.keywordsInternet of Thingsen_US
dc.description.acknowledgementThis research/project is supported by the National Research Foundation and the Smart Nation Digital Government Office, Prime Minister’s Office, Singapore under the Translational R&D for Smart Nation (TRANS Grant) Funding Initiative. The research work on direction of arrival estimation is also supported by the Singapore Ministry of Education Academic Research Fund Tier-2, under research grant MOE2017-T2-2-060.en_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Journal Articles
Files in This Item:
File Description SizeFormat 
Manuscript (PDF).pdf1.34 MBAdobe PDFView/Open

Page view(s)

Updated on Jul 6, 2022

Download(s) 50

Updated on Jul 6, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.