Debris Flow Infrasound Signal Recognition Approach Based on Improved AlexNet
Environmental interference noise is the main challenge for on-site monitoring of debris flow infrasound,which greatly limits the accuracy of debris flow infrasound signal identification.In view of the performance of deep learning in acoustic signal recognition,this paper proposes a debris flow infrasound signal recognition method based on improved AlexNet network,which effectively improves the accuracy and convergence speed of debris flow infrasound signal recognition.Firstly,the original infra-sound data set is preprocessed such as data expansion,filtering and noise reduction,and wavelet transform is used to generate a time-frequency spectrum image.Then the obtained time-frequency spectrum image is used as input,and an improved AlexNet network model is built by reducing the convolution kernel,introducing a batch normalization layer and selecting the Adam opti-mization algorithm.Experimental results show that the improved AlexNet network model has a recognition accuracy of 91.48%,achieves intelligent identification of debris flow infrasound signals and provides efficient and reliable technical support for debris flow infrasound monitoring and early warning.
debris flowinfrasounddeep learningmonitoring and early warningsignal recognition
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