Abstract
Infrasound is a type of low frequency signal that occurs in nature and results from man-made events, typically ranging in frequency from 0.01 Hz to 20 Hz. In this paper, a classification method based on Hilbert-Huang transform (HHT) and support vector machine (SVM) is proposed to discriminate between three different natural events. The frequency spectrum characteristics of infrasound signals produced by different events, such as volcanoes, are unique, which lays the foundation for infrasound signal classification. First, the HHT method was used to extract the feature vectors of several kinds of infrasound events from the Hilbert marginal spectrum. Then, the feature vectors were classified by the SVM method. Finally, the present of classification and identification accuracy are given. The simulation results show that the recognition rate is above 97.7%, and that approach is effective for classifying event types for small samples.
Highlights
Infrasound is a type of low frequency signal that is undetectable to the human ear, ranging in frequency from 0.01 to 20 Hz
We can analyze the characteristics of detected infrasound signals to complete classification, which is the technical basis of the global infrasound detection system, one of the four types of monitoring used by the Comprehensive Nuclear-Test-Ban Treaty (CTBT) International Monitoring System (IMS)
They were obtained using the method described previously. These results show that there is a high degree of similarity among the feature vectors in each class, while there are clear differences between classes
Summary
Infrasound is a type of low frequency signal that is undetectable to the human ear, ranging in frequency from 0.01 to 20 Hz. We can analyze the characteristics of detected infrasound signals to complete classification, which is the technical basis of the global infrasound detection system, one of the four types of monitoring used by the Comprehensive Nuclear-Test-Ban Treaty (CTBT) International Monitoring System (IMS). Infrasound classification consists of two parts: feature extraction and classification recognition. The key to classification of infrasonic events is the extraction of effective feature vectors from a signal. Feature vectors must be distinguished from other targets. Effective feature extraction techniques are the foundation of the classification of infrasound events. After feature extraction of the target signal, feature vectors must be processed by a classifier. The feature vectors largely determine classification, the performance of the classifier directly affects the classification results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
