Infrasound signal classification based on spectral entropy and support vector machine

• We propose an intelligent classification method for infrasound signal.
• We applied spectral entropy method to features vectors extraction.
• The proposed method improved the operation efficiency of classification algorithm.
• This study makes contribution to the real-time monitoring system of infrasound.

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.