Application of Combined Filtering in Thunder Recognition

Abstract

Thunder recognition is of great interest in lightning detection and physics and is widely used in short-range lightning location. However, due to the complexity of thunder, any single filtering method that is used in traditional speech noise reduction technology cannot identify well thunder from complicated background noise. In this study, the impact of four different filters on thunder recognition is compared, including low-pass filtering, least-mean-square adaptive filtering, spectral subtraction filtering, and Wiener filtering. The original acoustic signal and that filtered using different techniques are applied to a convolutional neural network, in which the thunder and background noise are classified. The results indicate that a combination of spectral subtraction and a low-pass filter performs the best in thunder recognition. The signal-to-noise ratio can be significantly improved, and the accuracy of thunder recognition (93.18%) can be improved by 3.8–18.6% after the acoustic signal is filtered using the combined filtering method. In addition, after filtering, the endpoints of a thunder signal can be better identified using the frequency domain sub-band variance algorithm.