A Wind Noise Detection Algorithm for Monitoring Infrasound Using Smartphone as a Sensor Device

  • Ryouichi NishimuraEmail author
  • Shuichi Sakamoto
  • Yôiti Suzuki
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 82)


Infrasound monitoring is promising for early warning systems to mitigate damage of disaster. However, wind noise contains the same frequency components as infrasound does, and they need to be separated. To achieve this purpose, a wind noise detection algorithm is proposed. Unlike conventional methods that typically use two microphones, the proposed method assumes that one pressure and one acoustic sensor is available. This assumption comes from a requirement that a smartphone is used as a sensor device. Wind noise is detected as anomaly detection of the microphone signal, using extreme value distribution. Comparing with the data obtained by an anemometer, it is shown that the proposed method successfully determines time periods where wind noise exists under a practical environment, depending on the condition of wind.



The authors would like to thank to Dr. Suzuki at NICT for providing the data recorded by the anemometer. This work is partly supported by JSPS KAKENHI (17K01351).


  1. 1.
    Elko, G.: Reducing noise in audio systems. US Patent 7,171,008 (2007)Google Scholar
  2. 2.
    Eskin, E.: Anomaly detection over noisy data using learned probability distributions. In: Proceedings of the International Conference on Machine Learning, pp. 255–262. Morgan Kaufmann (2000)Google Scholar
  3. 3.
    Feng, S., Nadarajah, S., Hu, Q.: Modeling annual extreme precipitation in China using the generalized extreme value distribution. J. Meteorol. Soc. Jpn. Ser. II 85(5), 599–613 (2007)CrossRefGoogle Scholar
  4. 4.
    McNeil, A.J., Frey, R.: Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. J. Empirical Finan. 7(3–4), 271–300 (2000)CrossRefGoogle Scholar
  5. 5.
    Observation system of the patch of blue sky for optical communication (OBSOC).
  6. 6.
    Patcha, A., Park, J.M.: An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2000)CrossRefGoogle Scholar
  7. 7.
    Pichon, A.L., Blanc, E., Hauchecorne, A. (eds.): Infrasound Monitoring for Atmospheric Studies. Springer, New York (2010)Google Scholar
  8. 8.
    Rasmussen, K., Frederiksen, P., Rasmussen, F., Petersen, K.: Wind noise insensitive hearing aid. US Patent 7,181,030 (2007)Google Scholar
  9. 9.
    Zakis, J.A., Tan, C.M.: Robust wind noise detection. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3655–3659, May 2014Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ryouichi Nishimura
    • 1
    Email author
  • Shuichi Sakamoto
    • 2
  • Yôiti Suzuki
    • 2
  1. 1.Resilient ICT Research Center, National Institute of Information and Communications Technology (NICT)SendaiJapan
  2. 2.Research Institute of Electrical CommunicationTohoku UniversitySendaiJapan

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