卷积混合音频源分离

N. Mitianoudis, M. Davies
{"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}
引用次数: 153

摘要

在现代文学中,在现实世界中记录的音源分离问题已经得到了很好的证实。一种解决这一问题的方法是利用独立分量分析(ICA)进行盲源分离。记录环境通常被建模为卷积。以往瞬态混合物ICA的研究为卷积混合物的分离提供了坚实的背景。作者修改了现有的方法,提出了一个快速的频域ICA框架,为这些方法中遇到的明显排列问题提供了解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Audio source separation of convolutive mixtures
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信