- Tytuł:
- Infrasound Signal Classification Based on ICA and SVM
- Autorzy:
-
Lu, Quanbo
Wang, Meng
Li, Mei - Powiązania:
- https://bibliotekanauki.pl/articles/31339863.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Czasopisma i Monografie PAN
- Tematy:
-
independent component analysis
fast Fourier transform
support vector machine
infrasound signal - Opis:
- 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.
- Źródło:
-
Archives of Acoustics; 2023, 48, 3; 191-199
0137-5075 - Pojawia się w:
- Archives of Acoustics
- Dostawca treści:
- Biblioteka Nauki
Artykuł