{"title":"音频特征提取的混合独立分量分析和粗糙集方法","authors":"Xin He, Ling Guo, Jianyu Wang, Xianzhong Zhou","doi":"10.1109/CCPR.2008.86","DOIUrl":null,"url":null,"abstract":"Audio classification is based on audio features. The choice of audio features can reflect important audio classification features in time and frequency time. The extraction and analysis of audio features are the base and important of audio classification. The most important problem is to extract audio features effectively and make them mutual independence to reduce information redundancy. In this paper, combined with independent component analysis and rough set, a method for audio feature extraction is presented and it's proved better performance by experiments.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"80 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid Independent Component Analysis and Rough Set Approach for Audio Feature Extraction\",\"authors\":\"Xin He, Ling Guo, Jianyu Wang, Xianzhong Zhou\",\"doi\":\"10.1109/CCPR.2008.86\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Audio classification is based on audio features. The choice of audio features can reflect important audio classification features in time and frequency time. The extraction and analysis of audio features are the base and important of audio classification. The most important problem is to extract audio features effectively and make them mutual independence to reduce information redundancy. In this paper, combined with independent component analysis and rough set, a method for audio feature extraction is presented and it's proved better performance by experiments.\",\"PeriodicalId\":292956,\"journal\":{\"name\":\"2008 Chinese Conference on Pattern Recognition\",\"volume\":\"80 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Chinese Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCPR.2008.86\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Chinese Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCPR.2008.86","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hybrid Independent Component Analysis and Rough Set Approach for Audio Feature Extraction
Audio classification is based on audio features. The choice of audio features can reflect important audio classification features in time and frequency time. The extraction and analysis of audio features are the base and important of audio classification. The most important problem is to extract audio features effectively and make them mutual independence to reduce information redundancy. In this paper, combined with independent component analysis and rough set, a method for audio feature extraction is presented and it's proved better performance by experiments.