Extracting urban sound information for residential areas in smart cities using an end-to-end IoT system
With 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 in...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/153454 |
| _version_ | 1826117491360792576 |
|---|---|
| author | Tan, Ee-Leng Karnapi, Furi Andi Ng, Linus Junjia Ooi, Kenneth Gan, Woon-Seng |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Tan, Ee-Leng Karnapi, Furi Andi Ng, Linus Junjia Ooi, Kenneth Gan, Woon-Seng |
| author_sort | Tan, Ee-Leng |
| collection | NTU |
| description | With 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. |
| first_indexed | 2024-10-01T04:28:28Z |
| format | Journal Article |
| id | ntu-10356/153454 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:28:28Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1534542021-12-03T06:51:21Z Extracting urban sound information for residential areas in smart cities using an end-to-end IoT system Tan, Ee-Leng Karnapi, Furi Andi Ng, Linus Junjia Ooi, Kenneth Gan, Woon-Seng School of Electrical and Electronic Engineering Centre for Information Sciences and Systems Engineering::Electrical and electronic engineering Acoustic Source Event Detection Deep Neural Networks Edge Analytics Edge-Cloud Architecture Internet of Things With 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. Ministry of Education (MOE) National Research Foundation (NRF) Accepted version This 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. 2021-12-03T06:50:18Z 2021-12-03T06:50:18Z 2021 Journal Article Tan, 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. https://dx.doi.org/10.1109/JIOT.2021.3068755 2327-4662 https://hdl.handle.net/10356/153454 10.1109/JIOT.2021.3068755 18 8 14308 14321 en MOE2017-T2-2-060 IEEE Internet of Things Journal © 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: https://doi.org/10.1109/JIOT.2021.3068755 application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Acoustic Source Event Detection Deep Neural Networks Edge Analytics Edge-Cloud Architecture Internet of Things Tan, Ee-Leng Karnapi, Furi Andi Ng, Linus Junjia Ooi, Kenneth Gan, Woon-Seng Extracting urban sound information for residential areas in smart cities using an end-to-end IoT system |
| title | Extracting urban sound information for residential areas in smart cities using an end-to-end IoT system |
| title_full | Extracting urban sound information for residential areas in smart cities using an end-to-end IoT system |
| title_fullStr | Extracting urban sound information for residential areas in smart cities using an end-to-end IoT system |
| title_full_unstemmed | Extracting urban sound information for residential areas in smart cities using an end-to-end IoT system |
| title_short | Extracting urban sound information for residential areas in smart cities using an end-to-end IoT system |
| title_sort | extracting urban sound information for residential areas in smart cities using an end to end iot system |
| topic | Engineering::Electrical and electronic engineering Acoustic Source Event Detection Deep Neural Networks Edge Analytics Edge-Cloud Architecture Internet of Things |
| url | https://hdl.handle.net/10356/153454 |
| work_keys_str_mv | AT taneeleng extractingurbansoundinformationforresidentialareasinsmartcitiesusinganendtoendiotsystem AT karnapifuriandi extractingurbansoundinformationforresidentialareasinsmartcitiesusinganendtoendiotsystem AT nglinusjunjia extractingurbansoundinformationforresidentialareasinsmartcitiesusinganendtoendiotsystem AT ooikenneth extractingurbansoundinformationforresidentialareasinsmartcitiesusinganendtoendiotsystem AT ganwoonseng extractingurbansoundinformationforresidentialareasinsmartcitiesusinganendtoendiotsystem |