西安工程大学学报2019，Vol.33Issue(1)：57-62,6.DOI:10.13338/j.issn.1674-649x.2019.01.010
小波包样本熵的扬声器异常音特征提取方法
Feature extraction method of loudspeaker abnormal sound based on wavelet packet decomposition and sample entropy
摘要
Abstract
To classify the loudspeaker abnormal sound more accurately, a feature extraction method is proposed, in which wavelet packet decomposition and sample entropy are used. After preprocessing of pitch notching, the loudspeaker response signal is decomposed using wavelet packet decomposition of three levels. Sample entropy values of reconstructed signals are calculated to structure the feature vectors. In the small sample case, the results of experiment show that the SVM algorithm with wavelet packet decomposition and sample entropy feature extraction method achieves 93.33％ classification accuracy.It's 5％ higher than that of energy mean method, which proves the proposed method.关键词
扬声器/异常音/基频陷波/小波包分解/样本熵/特征提取/支持向量机/短时傅里叶变换Key words
loudspeaker/abnormal sound/pitch notching/wavelet packet decomposition/sample entropy/feature extraction/SVM/STFT分类
信息技术与安全科学引用本文复制引用
王鸿姗,周静雷,房乔楚..小波包样本熵的扬声器异常音特征提取方法[J].西安工程大学学报,2019,33(1):57-62,6.基金项目
陕西省教育厅专项科研项目(11JK0548) （11JK0548）
