摘要
针对复杂环境中的声目标特征提取与选择问题,结合声信号时频域的特点,提出了一种时频域相结合的特征提取方法。首先,对信号进行小波分解,达到去噪目的;然后,将短时能量、短时平均幅值、过零率及频带能量值作为原始特征矢量,并结合Fisher判别准则进行特征选择,以此构造低维特征向量;最后,对两类声目标的实测样本数据进行特征提取,并采用支持向量机和K近邻两种分类器对该特征提取方法的有效性进行校验。实验结果表明,采用“时域+频域+线性判别分析”的特征提取方法简单有效,且与单一时域或频域的特征提取方法相比,识别率更高。
In view of the feature extraction and choice problem of acoustic target in complex environment, based on the time-frequency characteristics of acoustic signals, an effective method of feature extraction for acoustic signal is presented. Firstly, a method of wavelet decomposition is employed for signal de-noising. Secondly , taking the short-time energy, the short-time average amplitude, the zero crossing rate and the energy of signals' frequency bands as initial features, the low-dimensional feature vectors are constructed by combining the Fisher discriminant criterion. Finally, the features of the testing sample data of two types of acoustic targets are extracted, and the validity of the feature extraction method is verified by using support vector machine and K-nearest neighbor classifier. The experimental results show that the feature extraction method of “time domain + frequency domain + linear discriminant analysis” is simple and effective, and it shows higher recognition rate compared with single feature extraction methods.
作者
刘立芳
杨海霞
齐小刚
LIU Lifang;YANG Haixia;QI Xiaogang(School of Computer Science and Technology, Xidian University, Xi’an 710071, China;School of Mathematics and Statistics, Xidian University, Xi’an 710071, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2019年第10期2184-2190,共7页
Systems Engineering and Electronics
基金
国家自然科学基金项目(61877067)资助课题
关键词
小波分解
特征提取
线性判别分析
支持向量机
K近邻
wavelet decomposition
feature extraction
linear discriminant analysis
support vector machine
K-nearest neighbor
