QQ客服 官方微博 反馈留言

	Blind source separation of more sources than mixtures using sparse mixture models
	Shi, ZW ; Tang, HW ; Tang, YY
	2010-05-06 ; 2010-05-06
关键词	blind source separation overcomplete representation sparse mixture model independent component analysis signal processing INDEPENDENT COMPONENT ANALYSIS OVERCOMPLETE REPRESENTATIONS LEARNING SPARSE ALGORITHM Computer Science, Artificial Intelligence
中文摘要	In this paper, blind source separation is discussed with more sources than mixtures. This blind separation technique assumes a linear mixing model and involves two steps: (1) learning the mixing matrix for the observed data using the sparse mixture model and (2) inferring the sources by solving a linear programming problem after the mixing matrix is estimated. Through the experiments of the speech signals, we demonstrate the efficacy of this proposed approach. (c) 2005 Elsevier B.V. All rights reserved.
语种	英语 ; 英语
出版者	ELSEVIER SCIENCE BV ; AMSTERDAM ; PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/9065]
专题	清华大学
推荐引用方式 GB/T 7714	Shi, ZW,Tang, HW,Tang, YY. Blind source separation of more sources than mixtures using sparse mixture models[J],2010, 2010.
APA	Shi, ZW,Tang, HW,&Tang, YY.(2010).Blind source separation of more sources than mixtures using sparse mixture models..
MLA	Shi, ZW,et al."Blind source separation of more sources than mixtures using sparse mixture models".(2010).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。