{"title":"基于内容的音乐体裁分类中重要声学特征的分析与比较","authors":"Zhe Wang, Jingbo Xia, Bin Luo","doi":"10.1109/ITA.2013.99","DOIUrl":null,"url":null,"abstract":"Digital music is becoming increasingly popular in the Internet, and content-based musical genre classification has gained significant attentions in the field of musical retrieval. In this paper, the acoustic musical features are extracted from the viewpoints of both signal processing and the musical dimension. By comparing the performance of classifier of different combination of acoustic features, the contributions of corresponding features are evaluated. Finally, timbre and tonality feature sets are found to be the most effective features in music genre recognition.","PeriodicalId":285687,"journal":{"name":"2013 International Conference on Information Technology and Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"The Analysis and Comparison of Vital Acoustic Features in Content-Based Classification of Music Genre\",\"authors\":\"Zhe Wang, Jingbo Xia, Bin Luo\",\"doi\":\"10.1109/ITA.2013.99\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Digital music is becoming increasingly popular in the Internet, and content-based musical genre classification has gained significant attentions in the field of musical retrieval. In this paper, the acoustic musical features are extracted from the viewpoints of both signal processing and the musical dimension. By comparing the performance of classifier of different combination of acoustic features, the contributions of corresponding features are evaluated. Finally, timbre and tonality feature sets are found to be the most effective features in music genre recognition.\",\"PeriodicalId\":285687,\"journal\":{\"name\":\"2013 International Conference on Information Technology and Applications\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Information Technology and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITA.2013.99\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Information Technology and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITA.2013.99","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Analysis and Comparison of Vital Acoustic Features in Content-Based Classification of Music Genre
Digital music is becoming increasingly popular in the Internet, and content-based musical genre classification has gained significant attentions in the field of musical retrieval. In this paper, the acoustic musical features are extracted from the viewpoints of both signal processing and the musical dimension. By comparing the performance of classifier of different combination of acoustic features, the contributions of corresponding features are evaluated. Finally, timbre and tonality feature sets are found to be the most effective features in music genre recognition.