{"title":"基于未消差小波包变换的非平稳信号分类","authors":"M. Plessis, J. Olivier","doi":"10.1109/ICWAPR.2010.5576377","DOIUrl":null,"url":null,"abstract":"A classifier for non-stationary signals is presented in this paper. A time-frequency signal representation is calculated using the undecimated wavelet packet transform. The classification is performed with a support vector machine. Only the highest valued wavelet coefficients are selected as features in order to reduce the effect of noise. This classifier is compared against a classifier using a Wigner-Ville representation on a wideband non-stationary signal. The classifier based on the undecimated wavelet transform achieved a higher classification accuracy. Using only the largest half of the wavelet coefficients increased the classification accuracy","PeriodicalId":219884,"journal":{"name":"2010 International Conference on Wavelet Analysis and Pattern Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-stationary signal classification using the undecimated wavelet packet transform\",\"authors\":\"M. Plessis, J. Olivier\",\"doi\":\"10.1109/ICWAPR.2010.5576377\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A classifier for non-stationary signals is presented in this paper. A time-frequency signal representation is calculated using the undecimated wavelet packet transform. The classification is performed with a support vector machine. Only the highest valued wavelet coefficients are selected as features in order to reduce the effect of noise. This classifier is compared against a classifier using a Wigner-Ville representation on a wideband non-stationary signal. The classifier based on the undecimated wavelet transform achieved a higher classification accuracy. Using only the largest half of the wavelet coefficients increased the classification accuracy\",\"PeriodicalId\":219884,\"journal\":{\"name\":\"2010 International Conference on Wavelet Analysis and Pattern Recognition\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Wavelet Analysis and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICWAPR.2010.5576377\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Wavelet Analysis and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWAPR.2010.5576377","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-stationary signal classification using the undecimated wavelet packet transform
A classifier for non-stationary signals is presented in this paper. A time-frequency signal representation is calculated using the undecimated wavelet packet transform. The classification is performed with a support vector machine. Only the highest valued wavelet coefficients are selected as features in order to reduce the effect of noise. This classifier is compared against a classifier using a Wigner-Ville representation on a wideband non-stationary signal. The classifier based on the undecimated wavelet transform achieved a higher classification accuracy. Using only the largest half of the wavelet coefficients increased the classification accuracy