LEARNING OPTIMAL FEATURES FOR MUSIC TRANSCRIPTION.Ming, Huaiping;Huang, Dongyan;Xie, Lei,等.2014 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND
INFORMATION PROCESSING (CHINASIP).Xian, PEOPLES R CHINA,2014/1/1.
Multiple sparse sources separation based on multichannel frequency domain adaptive filtering
文献类型:会议
作者:Chen, Xiaoyu[1]Fu, Zhong-Hua[2]Xie, Lei[3]
机构:[1]Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, Northwestern Polytechnical University, Xi'an, China [2]Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, Northwestern Polytechnical University, Xi'an, China [3]Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, Northwestern Polytechnical University, Xi'an, China
年:2011
通讯作者:Chen, X.(cxy313186@gmail.com)
会议名称:Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011
页码范围:108-112
会议地点:Xi'an, China
会议开始日期:2011-10-18
会议结束日期:2011-10-21
收录情况:EI(20124015499570)
人气指数:3802
浏览次数:3761
语言:外文
摘要:
Underdetermined sparse sources separation is a challenge problem especially in adverse environment, where there are often some non-sparse interferences or more than one sparse interferences located closely to the target sources. While in some applications, such as in-car or hands-free environments, references of the interferences (P ?? 2) coming from loudspeakers are available. Common sparse source separation approaches have not yet used these reference information, we call them traditional appr ...More
Underdetermined sparse sources separation is a challenge problem especially in adverse environment, where there are often some non-sparse interferences or more than one sparse interferences located closely to the target sources. While in some applications, such as in-car or hands-free environments, references of the interferences (P ?? 2) coming from loudspeakers are available. Common sparse source separation approaches have not yet used these reference information, we call them traditional approaches in this paper. We propose a FD-MENUET (Frequency domain aDaptive filtering based Multiple sENsor degenerate Unmixing Estimation Technique) approach, in which we get full use of those reference information to help to separate the target sources. Even if no reference is available, the approach would only degenerate to the traditional approaches. The experimental results show that the proposed approach is more general and could achieves better separation performance than the traditional one. ...Hide
dc:title:Multiple sparse sources separation based on multichannel frequency domain adaptive filtering
dc:creator:Chen, Xiaoyu;Fu, Zhong-Hua;Xie, Lei
dc:date: publishDate:2011-10-18
dc:type:会议
dc:format: Media:Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011
dc:identifier: LnterrelatedLiterature:Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011.Xi'an, China.
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