Automatic labeling method for underwater acoustic signal samples based on multilayer optimal convolution
The application of deep learning in underwater acoustic research often faces problems such as large data volume requirements and current sample size limitations.Herein,the best convolution kernel is selected using the similarity-based optimization method to extract representative features.Then,by exploring the layer feature fusion strategy,the multilayer convolution output is superimposed to obtain comprehensive feature infor-mation.This study proposes a multilayer optimized convolutional network model that can effectively solve such problems through optimization,feature fusion,and attention mechanisms.Finally,a reduction strategy is used to optimize the model,which effectively shortens the operation time.The experimental results reveal that the best annotation accuracy of the model on the data set is 1.12%of the high baseline model,and the running time is reduced by 43.5%.Therefore,this model improves the accuracy and efficiency of underwater acoustic signal labeling.
国家科技期刊平台
NSTL
万方数据
维普
