International Journal of Intelligent Information Systems

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Infrasound Source Identification Based on Spectral Moment Features

Received: 09 March 2016    Accepted: 05 April 2016    Published: 26 April 2016
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Abstract

Infrasound signals have a frequency range below the human hearing frequency range, and originate from different sources. Since these waves contain useful information about the occurrence of some important event, in this paper we intend to present a method for the recognition of sources of these signals. In the present paper, by using the feature spectral moment along with Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) and also selecting a subset from the feature which plays a more discriminative role for the signal sources, and then by using classifier ensembles, we reached a 98.1% precision in the infrasound source identification.

DOI 10.11648/j.ijiis.20160503.11
Published in International Journal of Intelligent Information Systems (Volume 5, Issue 3, June 2016)
Page(s) 37-41
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Feature Extraction, Spectral Moment, Feature Selection, Recognition, Infrasound, Classifier Ensembles

References
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Author Information
  • Computer Engineering Department, Engineering Faculty, Alzahra University, Tehran, Iran

  • Computer Engineering Department, Engineering Faculty, Alzahra University, Tehran, Iran

  • Electronic Engineering Department, Engineering Faculty, Shahed University, Tehran, Iran

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    Zahra Madankan, Noushin Riahi, Akbar Ranjbar. (2016). Infrasound Source Identification Based on Spectral Moment Features. International Journal of Intelligent Information Systems, 5(3), 37-41. https://doi.org/10.11648/j.ijiis.20160503.11

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    ACS Style

    Zahra Madankan; Noushin Riahi; Akbar Ranjbar. Infrasound Source Identification Based on Spectral Moment Features. Int. J. Intell. Inf. Syst. 2016, 5(3), 37-41. doi: 10.11648/j.ijiis.20160503.11

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    AMA Style

    Zahra Madankan, Noushin Riahi, Akbar Ranjbar. Infrasound Source Identification Based on Spectral Moment Features. Int J Intell Inf Syst. 2016;5(3):37-41. doi: 10.11648/j.ijiis.20160503.11

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  • @article{10.11648/j.ijiis.20160503.11,
      author = {Zahra Madankan and Noushin Riahi and Akbar Ranjbar},
      title = {Infrasound Source Identification Based on Spectral Moment Features},
      journal = {International Journal of Intelligent Information Systems},
      volume = {5},
      number = {3},
      pages = {37-41},
      doi = {10.11648/j.ijiis.20160503.11},
      url = {https://doi.org/10.11648/j.ijiis.20160503.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijiis.20160503.11},
      abstract = {Infrasound signals have a frequency range below the human hearing frequency range, and originate from different sources. Since these waves contain useful information about the occurrence of some important event, in this paper we intend to present a method for the recognition of sources of these signals. In the present paper, by using the feature spectral moment along with Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) and also selecting a subset from the feature which plays a more discriminative role for the signal sources, and then by using classifier ensembles, we reached a 98.1% precision in the infrasound source identification.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Infrasound Source Identification Based on Spectral Moment Features
    AU  - Zahra Madankan
    AU  - Noushin Riahi
    AU  - Akbar Ranjbar
    Y1  - 2016/04/26
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ijiis.20160503.11
    DO  - 10.11648/j.ijiis.20160503.11
    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
    SP  - 37
    EP  - 41
    PB  - Science Publishing Group
    SN  - 2328-7683
    UR  - https://doi.org/10.11648/j.ijiis.20160503.11
    AB  - Infrasound signals have a frequency range below the human hearing frequency range, and originate from different sources. Since these waves contain useful information about the occurrence of some important event, in this paper we intend to present a method for the recognition of sources of these signals. In the present paper, by using the feature spectral moment along with Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) and also selecting a subset from the feature which plays a more discriminative role for the signal sources, and then by using classifier ensembles, we reached a 98.1% precision in the infrasound source identification.
    VL  - 5
    IS  - 3
    ER  - 

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