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Noise Sensing Calibration Under Different Phone Context

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Mobile Computing, Applications, and Services (MobiCASE 2019)

Abstract

Noise pollution severely threatens human well-being. Constructing a noise map based on crowd-sensing can help city planners better understand environmental noise at lower cost. Based on the strict sampling method limitation of state-of-the-art techniques, we build a new calibration model aimed at the pocket situations and the scenarios happened more frequently in actual life. The proposed model consists of a Activity Recognition Model (ARM) and a Signal Processing Model (SPM). Three types of data are taken into consideration, which are sitting, standing, and walking. In ARM, we collect 3-axis accelerometer data to identify current sampling context based on the convolutional neural network. SPM mainly implements noise level measurement and calibration according to the corresponding output of ARM under different phone context. The average errors after calibration are controlled to be within ±3 dB(A), and the classification precision reaches 99.2%. Finally, we display the noise map adopting different criteria based on the building types, which is more scientific and meaningful. The final results show that our proposed calibration model is feasible and can improve the data quality under different situations.

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Authors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Min Huang & Lina Chen

Authors
  1. Min Huang
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  2. Lina Chen
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Corresponding author

Correspondence to Min Huang .

Editor information

Editors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, China

    Yuyu Yin

  2. Zhejiang University, Hangzhou, China

    Ying Li

  3. Shanghai University, Shanghai, China

    Honghao Gao

  4. Hangzhou Dianzi University, Hangzhou, China

    Jilin Zhang

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Huang, M., Chen, L. (2019). Noise Sensing Calibration Under Different Phone Context. In: Yin, Y., Li, Y., Gao, H., Zhang, J. (eds) Mobile Computing, Applications, and Services. MobiCASE 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-030-28468-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-28468-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28467-1

  • Online ISBN: 978-3-030-28468-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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