应用最优小波包变换的特征提取方法

在模式识别或分类中,从原始模式中提取有效的分类特征是非常重要的.但对于大量的非平稳或时变信号模式来说,如语音,雷达,地震信号等,用于分类的特征往往包含在局部的时-频信息中,用一般的变换方法提取有效的特征比较困难.近年来小波变换在信号处理和特征提取中得到了广泛应用,但小波包变换的任意多尺度分解特性,是分析非平稳信号更有效的方法,这是由于小波库中包含了丰富的小波包基,不同的小波包基具有不同的性质,反映不同的信号特性,能获取其他变换所不能获取的信号特征.本文主要研究由给定的训练样本集,如何选择最优小波包基,从被识别或分类的信号中提取具有最大可分性的特征.为此提出了应用三种可分性准则,即距离准则,散度准则和熵准则选择最优基.通过实验,对应用各准则选择最优基提取特征与小波基提取特征的性能进行了比较.

Feature Extraction Using Best Wavelet Packet Transform

In pattern recognition or classification,extracting effective classification features from original pattern signals is very important.But,for a great number of non-stationary or time-varying signals,such as speech,radar,earthquake signals,etc.,classification features are often localized both in time and frequency,so thus extracting effective features from them by general transformation methods is very difficult.Wavelet packet transform can provides an arbitrary time-frequency decomposition for the signals,because a wavelet packet library contains many wavelet packet bases,which can handle the different components of a signal.Therefore,by selecting a suitable basis,the effective features can be extracted.This paper is mainly concerned with extracting effective features from the recognized or classified signals by selecting wavelet packet basis via given training sample sets.Three separability criteria,i.e.,distance criterion,divergence criterion and entropy criterion,are used for selecting the best basis.The performance of features extraction by wavelet packet transform is compared with that by wavelet transform through experiments.