@ARTICLE{Lu_Quanbo_Infrasound_2023,
 author={Lu, Quanbo and Wang, Meng and Li, Mei},
 volume={vol. 48},
 number={No 2},
 journal={Archives of Acoustics},
 pages={191-199},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics},
 abstract={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.},
 type={Article},
 title={Infrasound Signal Classification Based on ICA and SVM},
 URL={http://journals.pan.pl/Content/127336/PDF/aoa.2023.145230.pdf},
 doi={10.24425/aoa.2023.145230},
 keywords={independent component analysis, fast Fourier transform, support vector machine, infrasound signal},
}