{"title":"卷积混合音频源分离","authors":"N. Mitianoudis, M. Davies","doi":"10.1109/TSA.2003.815820","DOIUrl":null,"url":null,"abstract":"The problem of separation of audio sources recorded in a real world situation is well established in modern literature. A method to solve this problem is blind source separation (BSS) using independent component analysis (ICA). The recording environment is usually modeled as convolutive. Previous research on ICA of instantaneous mixtures provided solid background for the separation of convolved mixtures. The authors revise current approaches on the subject and propose a fast frequency domain ICA framework, providing a solution for the apparent permutation problem encountered in these methods.","PeriodicalId":13155,"journal":{"name":"IEEE Trans. Speech Audio Process.","volume":"59 5","pages":"489-497"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"153","resultStr":"{\"title\":\"Audio source separation of convolutive mixtures\",\"authors\":\"N. Mitianoudis, M. Davies\",\"doi\":\"10.1109/TSA.2003.815820\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of separation of audio sources recorded in a real world situation is well established in modern literature. A method to solve this problem is blind source separation (BSS) using independent component analysis (ICA). The recording environment is usually modeled as convolutive. Previous research on ICA of instantaneous mixtures provided solid background for the separation of convolved mixtures. The authors revise current approaches on the subject and propose a fast frequency domain ICA framework, providing a solution for the apparent permutation problem encountered in these methods.\",\"PeriodicalId\":13155,\"journal\":{\"name\":\"IEEE Trans. Speech Audio Process.\",\"volume\":\"59 5\",\"pages\":\"489-497\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"153\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Trans. Speech Audio Process.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TSA.2003.815820\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Trans. Speech Audio Process.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TSA.2003.815820","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The problem of separation of audio sources recorded in a real world situation is well established in modern literature. A method to solve this problem is blind source separation (BSS) using independent component analysis (ICA). The recording environment is usually modeled as convolutive. Previous research on ICA of instantaneous mixtures provided solid background for the separation of convolved mixtures. The authors revise current approaches on the subject and propose a fast frequency domain ICA framework, providing a solution for the apparent permutation problem encountered in these methods.