Infrasound Source Identification Based on Spectral Moment Features

Zahra Madankan; Noushin Riahi; Akbar Ranjbar

doi:doi:10.11648/j.ijiis.20160503.11

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Infrasound Source Identification Based on Spectral Moment Features

Zahra Madankan, Noushin Riahi, Akbar Ranjbar

Published in International Journal of Intelligent Information Systems (Volume 5, Issue 3)

Received: 9 March 2016 Accepted: 5 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.

Published in	International Journal of Intelligent Information Systems (Volume 5, Issue 3)
DOI	10.11648/j.ijiis.20160503.11
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), 2016. Published by Science Publishing Group

Keywords

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

References

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APA Style

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://article.sciencepublishinggroup.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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Author Information

Zahra Madankan

Computer Engineering Department, Engineering Faculty, Alzahra University, Tehran, Iran
Noushin Riahi

Computer Engineering Department, Engineering Faculty, Alzahra University, Tehran, Iran
Akbar Ranjbar

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

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APA Style

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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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