Infrasound signal classification based on spectral entropy and support vector machine

被引：19

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

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