From Blind to Guided Audio Source Separation [How models and side information can improve the separation of sound]

被引：90

作者：

Vincent, Emmanuel ^{[1
,2
]}

Bertin, Nancy ^{[3
]}

Gribonval, Remi ^{[4
]}

Bimbot, Frederic ^{[4
,5
,6
]}

机构：

[1] Inria, Nancy, France

[2] Univ London, Ctr Digital Music Queen Mary, London WC1E 7HU, England

[3] French Natl Ctr Sci Res CNRS, Paris, France

[4] Inria, Rennes, France

[5] CNRS, ENST, F-75700 Paris, France

[6] CNRS, IRISA, Rennes, France

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2014年 / 31卷 / 03期

关键词：

NONNEGATIVE MATRIX FACTORIZATION; MIXTURES; PRIORS;

D O I：

10.1109/MSP.2013.2297440

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Audio is a domain where signal separation has long been considered as a fascinating objective, potentially offering a wide range of new possibilities and experiences in professional and personal contexts, by better taking advantage of audio material and finely analyzing complex acoustic scenes. It has thus always been a major area for research in signal separation and an exciting challenge for industrial applications. © 1991-2012 IEEE.

引用

页码：107 / 115

页数：9

共 33 条

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