Archives of Acoustics, 48, 2, pp. 191–199, 2023

Infrasound Signal Classification Based on ICA and SVM

Quanbo LU
ORCID ID 0000-0003-0941-4835
China University of Geosciences

China University of Geosciences

Mei LI
China University of Geosciences

A diagnostic technique based on independent component analysis (ICA), fast Fourier transform (FFT), and support vector machine (SVM) is suggested for effectively extracting signal features in infrasound signal monitoring. Firstly, ICA is proposed to separate the source signals of mixed infrasound sources. Secondly, FFT is used to obtain the feature vectors of infrasound signals. Finally, SVM is used to classify the extracted feature vectors. The approach integrates the advantages of ICA in signal separation and FFT to extract the feature vectors. An experiment is conducted to verify the benefits of the proposed approach. The experiment results demonstrate that the classification accuracy is above 98.52% and the run time is only 2.1 seconds. Therefore, the proposed strategy is beneficial in enhancing geophysical monitoring performance.
Keywords: independent component analysis; fast Fourier transform; support vector machine; infrasound signal
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Albert S., Linville L. (2020), Benchmarking current and emerging approaches to infrasound signal classification, Seismological Research Letters, 91(2A): 921– 929, doi: 10.1785/0220190116.

Amarnath M. (2016), Local fault assessment in a helical geared system via sound and vibration parameters using multiclass SVM classifiers, Archives of Acoustics, 41(3): 559–571, doi: 10.1515/aoa-2016-0054.

Cannata A. et al. (2011), Clustering and classification of infrasonic events at Mount Etna using pattern recognition techniques, Geophysical Journal International, 185(1): 253–264, doi: 10.1111/j.1365-246X.2011.04951.x.

Cárdenas-Peña D., Orozco-Alzate M., Castellanos-Dominguez G. (2013), Selection of time-variant features for earthquake classification at the Nevado-del-Ruiz volcano, Computers & Geosciences, 51: 293–304, doi: 10.1016/j.cageo.2012.08.012.

Chernogor L.F., Shevelev N.B. (2018), Characteristics of the infrasound signal generated by Chelyabinsk celestial body: Global statistics, Radio Physics and Radio Astronomy, 23(1): 24–35, doi: 10.15407/rpra23.01.024.

Cooley J.W., Tukey J.W. (1965), An algorithm for the machine calculation of complex Fourier series, Mathematics of Computation, 19(90): 297–301, doi: 10.1090/S0025-5718-1965-0178586-1.

Cortes C., Vapnik V. (1995), Support-vector networks, Machine Learning, 20: 273–297, doi: 10.1007/BF00994018.

Gi N., Brown P. (2017), Refinement of bolide characteristics from infrasound measurements, Planetary and Space Science, 143: 169–181, doi: 10.1016/j.pss.2017.04.021.

Ham F.M., Rekab K., Acharyya R., Lee Y.C. (2008), Infrasound signal classification using parallel RBF Neural Networks, International Journal of Signal and Imaging Systems Engineering, 1(3–4): 155–167, doi: 10.1504/IJSISE.2008.026787.

Iezzi A.M., Schwaiger H.F., Fee D., Haney M.M. (2019), Application of an updated atmospheric model to explore volcano infrasound propagation and detection in Alaska, Journal of Volcanology and Geothermal Research, 371: 192–205, doi: 10.1016/j.jvolgeores.2018.03.009.

Li M., Liu X.Y., Liu X. (2016), Infrasound signal classification based on spectral entropy and support vector machine, Applied Acoustics, 113: 116-120, doi: 10.1016/j.apacoust.2016.06.019.

Liu D., Tang D., Zhang S., Leng X., Hu K., He L. (2021), Method for feature analysis and intelligent recognition of infrasound signals of soil landslides, Bulletin of Engineering Geology and the Environment, 80: 917–932, doi: 10.1007/s10064-020-01982-w.

Liu X.Y., Li M., Tang W.,Wang S.C., Wu X. (2014), A new classification method of infrasound events using Hilbert-Huang transform and support vector machine, Mathematical Problems in Engineering, 2014(3): 1–6, doi: 10.1155/2014/456818.

Mayer S., Van Herwijnen A., Ulivieri G., Schweizer J. (2020), Evaluating the performance of an operational infrasound avalanche detection system at three locations in the Swiss Alps during two winter seasons, Cold Regions Science and Technology, 173: 102962, doi: 10.1016/j.coldregions.2019.102962.

McKee K., Fee D., Haney M., Matoza R.S., Lyons J. (2018), Infrasound signal detection and back azimuth estimation using ground-coupled airwaves on a seismo-acoustic sensor pair, Journal of Geophysical Research: Solid Earth, 123(8): 6826–6844, doi: 10.1029/2017JB015132.

Mika D., Kleczkowski P. (2011), ICA-based single channel audio separation: new bases and measures of distance, Archives of Acoustics, 36(2): 311–331, doi: 10.2478/v10168-011-0024-x.

Qian G., Wang L., Wang S., Duan S. (2019), A novel fixed-point algorithm for constrained independent component analysis, EURASIP Journal on Advances in Signal Processing, 2019(1): 28, doi: 10.1186/s13634-019-0622-8.

Sastry A.V., Hu A., Heckmann D., Poudel S., Kavvas E., Palsson B.O. (2021), Independent component analysis recovers consistent regulatory signals from disparate datasets, PLOS Computational Biology, 17(2): e1008647, doi: 10.1371/journal.pcbi.1008647.

Thüring T., Schoch M., Van Herwijnena A., Schweizer J. (2015), Robust snow avalanche detection using supervised machine learning with infrasonic sensor arrays, Cold Regions Science and Technology, 111: 60–66, doi: 10.1016/j.coldregions.2014.12.014.

Tsybul’skaya N.D., Kulichkov S.N., Chulichkov A.I. (2012), Studying possibilities for the classification of infrasonic signals from different sources, Izvestiya, Atmospheric and Oceanic Physics, 48(4): 384–390, doi: 10.1134/S0001433812040147.

Zhao J., Liu Y., Yang J. (2021), 3D matching positioning method for landslide using infrasound signal received by triangular pyramid vector array, based on ray theory, Bulletin of Engineering Geology and the Environment, 80(2): 889–904, doi: 10.1007/s10064-020-01988-4.

DOI: 10.24425/aoa.2023.145230