计算机与现代化Issue(3)：1-6,6.DOI:10.3969/j.issn.1006-2475.2024.03.001
基于改进AlexNet网络的泥石流次声信号识别方法
Debris Flow Infrasound Signal Recognition Approach Based on Improved AlexNet
摘要
Abstract
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.关键词
泥石流/次声/深度学习/监测预警/信号识别Key words
debris flow/infrasound/deep learning/monitoring and early warning/signal recognition分类
信息技术与安全科学引用本文复制引用
袁莉,刘敦龙,桑学佳,张少杰,陈乔..基于改进AlexNet网络的泥石流次声信号识别方法[J].计算机与现代化,2024,(3):1-6,6.基金项目
国家自然科学基金青年项目(42001100) （42001100）
四川省自然科学基金资助项目(2023NSFSC0751) （2023NSFSC0751）
四川省信息化应用支撑软件工程技术研究中心开放课题(760115027) （760115027）
