基于线性判别分析的时频域特征提取算法
TN912; 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....
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| Published in | 系统工程与电子技术 Vol. 41; no. 10; pp. 2184 - 2190 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
西安电子科技大学计算机科学与技术学院,陕西西安,710071%西安电子科技大学数学与统计学院,陕西西安,710071
01.10.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-506X |
| DOI | 10.3969/j.issn.1001-506X.2019.10.05 |
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| Abstract | TN912; 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. |
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| AbstractList | TN912; 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. |
| Author | 杨海霞 齐小刚 刘立芳 |
| AuthorAffiliation | 西安电子科技大学计算机科学与技术学院,陕西西安,710071%西安电子科技大学数学与统计学院,陕西西安,710071 |
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| Author_FL | QI Xiaogang LIU Lifang YANG Haixia |
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| Keywords | 支持向量机 特征提取 小波分解 K近邻 线性判别分析 |
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| Publisher | 西安电子科技大学计算机科学与技术学院,陕西西安,710071%西安电子科技大学数学与统计学院,陕西西安,710071 |
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| Title | 基于线性判别分析的时频域特征提取算法 |
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