Infrasound signal classification based on spectral entropy and support vector machine

被引：22

作者：

Li, Mei ^{[1
]}

Liu, Xueyong ^{[2
]}

Liu, Xu ^{[1
]}

机构：

[1] China Univ Geosci, Sch Informat Engn, Beijing, Peoples R China

[2] China Univ Geosci, Sch Humanities & Econ Management, Beijing, Peoples R China

来源：

APPLIED ACOUSTICS | 2016年 / 113卷

基金：

中国国家自然科学基金;

关键词：

Feature extraction; Spectral entropy; Infrasound signal; Support vector machines; Pattern recognition; EXTRACTION; SVM;

D O I：

10.1016/j.apacoust.2016.06.019

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

The operation speed of the algorithm is the critical factor in the real-time monitoring of infrasound signals. The existing methods mainly focus on how to improve the accuracy of classification and can't be used in real time monitoring because of their slow running speed. We adopt spectral entropy into the feature extraction of infrasound signals. Combined with the support vector machine algorithm, the proposed method effectively extracted the signal features meanwhile greatly improved the operation efficiency. Experimental results show that the running speed of the proposed method is 1.0 s, which is far less than 4.7 s of the comparison method. So the proposed method can be applied in real-time monitoring of earthquakes, tsunamis, landslides and other infrasound events. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：116 / 120

页数：5

共 50 条

[41] Classification Red Blood Cells Using Support Vector Machine
Akrimi, Jameela Ali
George, Loay E.
Suliman, Azizah
Ahmad, Abdul Rahim
PROCEEDINGS OF THE 2014 6TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND MULTIMEDIA (ICIM), 2014, : 265 - 269
[42] Quantum-Enhanced Support Vector Machine for Sentiment Classification
Ruskanda, Fariska Zakhralativa
Abiwardani, Muhammad Rifat
Mulyawan, Rahmat
Syafalni, Infall
Larasati, Harashta Tatimma
IEEE ACCESS, 2023, 11 : 87520 - 87532
[43] VMD and CNN-Based Classification Model for Infrasound Signal
Lu, Quanbo
Li, Mei
ARCHIVES OF ACOUSTICS, 2023, 48 (03) : 403 - 412
[44] Maximal Margin Support Vector Machine for Feature Representation and Classification
Lai, Zhihui
Chen, Xi
Zhang, Junhong
Kong, Heng
Wen, Jiajun
IEEE TRANSACTIONS ON CYBERNETICS, 2023, 53 (10) : 6700 - 6713
[45] Nonparallel hyperplane support vector machine for binary classification problems
Shao, Yuan-Hai
Chen, Wei-Jie
Deng, Nai-Yang
INFORMATION SCIENCES, 2014, 263 : 22 - 35
[46] State Classification Algorithm for Bus Based on Hierarchical Support Vector Machine
Xiao, Lizhong
Cheng, Long
2015 8TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2, 2015, : 649 - 652
[47] Pixel-based Classification Using Support Vector Machine Classifier
Varma, M. Krishna Satya
Rao, N. K. K.
Raju, K. K.
Varma, G. P. S.
2016 IEEE 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (IACC), 2016, : 51 - 55
[48] Automated classification of Wuyi rock tealeaves based on support vector machine
Lin, Li-Hui
Li, Cheng-Hsuan
Yang, Sheng
Li, Shao-Zi
Wei, Yi
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2019, 31 (23)
[49] Gas identification based on sensor array and support vector machine classification
Wang, XD
Zhang, HR
Wang, JS
Xu, XL
Du, H
PROCEEDINGS OF THE THIRD INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION SCIENCE AND TECHNOLOGY, VOL 1, 2004, : 763 - 767
[50] Instance-based entropy fuzzy support vector machine for imbalanced data
Poongjin Cho
Minhyuk Lee
Woojin Chang
Pattern Analysis and Applications, 2020, 23 : 1183 - 1202

← 1 2 3 4 5 →