基于小波包时频图特征和卷积神经网络的水声信号分类

Classification of underwater acoustic signals based on time-frequency map features of wavelet packet and convolutional neural network

水下声信号分类是水声学研究的一个重要方向。一个有效的特征提取和分类决策方法对水声信号分类技术至关重要。文章将鱼声、商船辐射噪声和风关噪声三类实测的水声信号在小波包分解的基础上提取时频图特征,并搭建了一个七层结构的卷积神经网络作为分类器。研究结果表明:三种水声信号的小波包时频图特征结合卷积神经网络在不同测试集可达到(98±1)%的总体准确率。因此,小波包时频图特征结合卷积神经网络的水声分类方法可望推广至更多水声信号分类。该研究结果可为水声信号的分类识别研究提供应用参考。

Underwater acoustic signal classification is an important research direction in underwater acoustics,and an effective feature extraction and classification decision method is of great concern for underwater acoustic signal classi-fication technology.In this paper,based on wavelet packet decomposition,the time-frequency map features of three kinds of underwater acoustic signals,namely fish vocalization signals,merchant ship radiated noise signals and wind-generated noise signals,are extracted,and a convolutional neural network(CNN)with seven-layer structure is set up as a classifier.The research results show that the accuracy of the three kinds of acoustic signals by the methods of combining the time-frequency map features of wavelet packet and the convolutional neural network can reach(98±1)%in different test sets.Therefore,the method can be expected to be used for the classification of more underwater acoustic signals.This research results can provide a reference for the classification and recognition of underwater acoustic signals.