Infrasound Event Classification Fusion Model Based on Multiscale SE-CNN and BiLSTM

The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters. The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction. However, guaranteeing the effectiveness of the extracted features is difficult. The current trend focuses on using a convolution neural network to automatically extract features for classification. This method can be used to extract signal spatial features automatically through a convolution kernel; however, infrasound signals contain not only spatial information but also temporal information when used as a time series. These extracted temporal features are also crucial. If only a convolution neural network is used, then the time dependence of the infrasound sequence will be missed. Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal. A multiscale squeeze excitation-convolution neural network-bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems. This model automatically extracted temporal and spatial features, adaptively selected features, and also realized the fusion of the two types of features. Experimental results showed that the classification accuracy of the model was more than 98%, thus verifying the effectiveness and superiority of the proposed model.