TFBSSpack
所属分类：matlab编程
开发工具：matlab
文件大小：25KB
下载次数：416
上传日期：2010-04-23 13:57:26
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patton5138
说明： TFBSS是一种基于短时傅里叶时频分析的盲源分离算法，算法基于卷积混合。处理非平稳源信号。
(TFBSS performs Blind Source Separation of (over)determined multiplicative mixtures of non-stationary real valued sources.
TFBSS is based on the joint-diagonalization of whitened and noise-compensated Spatial Time-Frequency Distribution (STFD) matrices of the observations, corresponding to single auto-terms positions, as described in:
C. Févotte and C. Doncarli. "Two contributions to blind source separation using time-frequency distributions", IEEE Signal Processing Letters, 2004. IEEE Signal Processing Letters, vol. 11, no. 3, Mar. 2004. pdf
and
A. Holobar, C. Févotte, C. Doncarli, and D. Zazula. "Single autoterms selection for blind source separation in time-frequency plane". In Proc. 11th EUSIPCO, Toulouse, France, 2002 (Special Session on Source Separation). pdf
)
文件列表:
TFBSSpack (0, 2003-09-03)
TFBSSpack\data.mat (6328, 2003-07-29)
TFBSSpack\demoTFBSS.m (4439, 2003-09-03)
TFBSSpack\joint_diag_rc.m (3873, 2003-07-29)
TFBSSpack\LICENSE.txt (15140, 2003-07-29)
TFBSSpack\tfbss.m (8674, 2003-09-03)
TFBSSpack\tfrspwv.m (4329, 2003-09-03)
TFBSSpack\window.m (5099, 2003-07-29)
******************************* The TFBSS code *************************************
TFBSS performs Blind Source Separation of (over)determined multiplicative mixtures
of non-stationary real valued sources.
TFBSS can be downloaded at http://www.irccyn.ec-nantes.fr/~fevotte/TFBSS_pack
TFBSS is based on the joint-diagonalization of whitened and noise-compensated
Spatial Time-Frequency Distribution (STFD) matrices of the observations,
corresponding to single auto-terms positions.
------------------------------------------------------------------------------------
The current main reference is:
A. Holobar, C. Févotte, C. Doncarli, and D. Zazula,
"Single autoterms selection for blind source separation in time-frequency plane",
In 11 e EUSIPCO, Toulouse, France, 3-6 septembre 2002.
The iterative selection of maxima of the criteria in the above paper has been
replaced by a more simple and computation friendly gradient approach to be published
soon. The inner Iterative Joint Diagonalization has not been implemented in TFBSS.
The first paper dealing with joint-diagonalization of STFD matrices is:
A. Belouchrani and M. G. Amin,
"Blind Source Separation Based on Time-Frequency Signal Representation'',
IEEE Trans. on Signal Processing, vol. 46. No. 11. pp. 2888-28***. November 19***.
------------------------------------------------------------------------------------
The TFBSS pack contains the following MATLAB files:
* tfbss.m : main program
* joint_diag.m : performs joint-diagonalization of complex matrices. Available on
J.F Cardoso web site (http://www.tsi.enst.fr/~cardoso/stuff.html) and used with
the kind permission of its author.
Copyright owned by J.F Cardoso - cardoso@enst.fr .
* tfrridb.m & window.m : perform TFDs computation. Excerpts from the Matlab
Time-Frequency Toolbox and used with the kind permission of their author F. Auger.
Copyright owned by F. Auger - f.auger@ieee.org .
Download the whole Matlab Time-Frequency Toolbox at:
http://crttsn.univ-nantes.fr/~auger/tftb.html
* demoTFBSS.m : demo script of TFBSS, separation of 4 noisy instantaneous mixtures of
3 Time-Varying ARMA sources.
* data.mat : contains the sources used in demoTFBSS.
For a quick overview of TFBSS performance, download all the files in a common
directory and call demoTFBSS.m in a MATLAB command window.
Feedback about your tests with real world signals is welcome.
Please mail me results.
Please report any bug.
Author: C. Févotte (cedric.fevotte@irccyn.ec-nantes.fr)
Copyright is owned by C. Févotte & A. Holobar
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TFBSS.m - Sept 9, 2002
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