Participatory sound meter calibration system for mobile devices: Poster abstract

Sheng Chun Wu, Dong Yi Wu, Fu Hsiang Ching, Ling Jyh Chen

研究成果: 書貢獻/報告類型會議論文篇章

摘要

Noise exposure has been the emerging environmental factor for human health. Yet an accurate and large-scale sound monitoring network is not available due to the expense of high-quality professional sound level meters and poorly-calibrated low-cost noise sensors. In this work, we propose a participatory sound meter calibration using smartphones. The system employs a low-cost and open-sourced calibration station to conduct side-by-side sound measurements, and all the measurement data are uploaded to the open data portal to build calibration models for different phone brands and models. We show that, using our calibration models, the MAE of calibration performance can be reduced significantly from 12.4 dbA to 2.8 dbA for the same device and 3.3 dbA for the other device of the same phone model. The results of this study can benefit crowdsourcing-based large-scale sound measurements and facilitate noise exposure, public health, and smart city researches in the future.

原文英語
主出版物標題SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems
發行者Association for Computing Machinery, Inc
頁面709-710
頁數2
ISBN(電子)9781450375900
DOIs
出版狀態已發佈 - 2020 十一月 16
事件18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020 - Virtual, Online, 日本
持續時間: 2020 十一月 162020 十一月 19

出版系列

名字SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems

會議

會議18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020
國家日本
城市Virtual, Online
期間2020/11/162020/11/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

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