Open AccessThunder Signal Detection via Deep Learning
Author(s) -
Han Zhang,
Binbin Yan,
Shanqiang Gu,
Cheolhong An,
Jian Li,
Min Wu,
Yu Wang,
Heng Xu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1828/1/012023
Subject(s) - thunder , computer science , artificial intelligence , convolutional neural network , deep learning , robustness (evolution) , mel frequency cepstrum , pattern recognition (psychology) , frame (networking) , speech recognition , computer vision , feature extraction , telecommunications , engineering , biochemistry , chemistry , electrical engineering , gene
In this paper, a thunder signal detection method is proposed based on the deep learning framework. The recorded thunder signal is segment-wise acquired, stored and pre-processed. In each frame, we use Mel Frequency Cepstrum Coefficient (MFCC) to extract the features of the thunder signal, which is consistent with the frequency characteristics of human perception. We then use the MFCC features derived in each frame to form a 3-channel tensor data, which is used as the further input to the designed convolutional neural network (CNN). The goal of CNN is to classify the existence of thunder for a single data frame. To improve the robustness of CNN, we included other confusing signals that are similar to thunder signals in the training and testing datasets. On the testing dataset, our proposed method outperforms the state-of-art methods in terms of accuracy, sensitivity, and specificity. Our proposed deep-learning-based thunder detection method not only increases the real-time performance of the lighting location system with thunder signals but also further improves the accuracy of other sound alarm systems.