{"title":"歌曲中乐器声音的分离","authors":"K. Youssef, P. Woo","doi":"10.1109/EIT.2008.4554343","DOIUrl":null,"url":null,"abstract":"This paper proposes a new solution to the audio source separation problem. The objective is to separate the original audio source signals generated by various musical instruments in one mixture. Existing approaches to solving this problem that humans easily cope with still have little success. In this paper, the blind source separation (BSS) is approached from a new point of view and is dealt with as a machine learning problem.","PeriodicalId":215400,"journal":{"name":"2008 IEEE International Conference on Electro/Information Technology","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Instrument sound separation in songs\",\"authors\":\"K. Youssef, P. Woo\",\"doi\":\"10.1109/EIT.2008.4554343\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new solution to the audio source separation problem. The objective is to separate the original audio source signals generated by various musical instruments in one mixture. Existing approaches to solving this problem that humans easily cope with still have little success. In this paper, the blind source separation (BSS) is approached from a new point of view and is dealt with as a machine learning problem.\",\"PeriodicalId\":215400,\"journal\":{\"name\":\"2008 IEEE International Conference on Electro/Information Technology\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Electro/Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EIT.2008.4554343\",\"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 IEEE International Conference on Electro/Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EIT.2008.4554343","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper proposes a new solution to the audio source separation problem. The objective is to separate the original audio source signals generated by various musical instruments in one mixture. Existing approaches to solving this problem that humans easily cope with still have little success. In this paper, the blind source separation (BSS) is approached from a new point of view and is dealt with as a machine learning problem.
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