Infrasound Signal Classification Based on Combining Spectral and Sound Features

The speed and accuracy of data classification are essential factors in the online monitoring of infrasound waves. the available techniques for classifying these data are relatively high accuracy but not high-speed online. in this paper, using the combination of sound and spectral features, the speed of runtime and accuracy of classification have been improved. this method uses spectral entropy, a spectral feature, and the linear predictive coding (lpc) sound features. the proposed approach classifies the infrasound waves generated from the three classes of the rocket launcher, volcano, and shuttle re-entry with a precision of 97% and a runtime of 1.0 seconds that are accurate to the existing methods. the obtained results show that this method has better speed and accuracy rather than existing methods.