perforICA
所属分类:matlab编程
开发工具:matlab
文件大小:882KB
下载次数:67
上传日期:2016-05-27 19:58:32
上 传 者:
大熊
说明: 基于matlab的ICA语音信号的盲源分离代码,里面包含实际的混合语音,分析结果,结果还是挺不错的。
(Based on Blind Source Separation of ICA matlab speech signal, which contains the actual mix of voice analysis results, the result was very good.)
文件列表:
perforICA (0, 2016-05-27)
perforICA\1.m (2179, 1998-07-23)
perforICA\2.wav (493352, 2010-05-08)
perforICA\3.wav (409100, 2010-08-24)
perforICA\decorrelation2.m (333, 1998-07-23)
perforICA\ica_f.m (1213, 1998-07-24)
perforICA\main.asv (1002, 2010-08-18)
perforICA\main.m (650, 2010-08-24)
perforICA\permutation1.m (1364, 1998-07-23)
perforICA\sepfilter.m (1151, 1998-07-23)
perforICA\X_linear22.wav (123244, 2003-06-23)
perforICA\X_room33.wav (66840, 2003-06-23)
ica_f.m bsepf2 is the main program and it needs
correlation.m
decorrelation.m
permutation.m
sepfilter.m.
You also need to visit
ftp://sig.enst.fr/pub/jfc/Algo/Joint_Diag/
and get a matlab code "joint_diag.m". this is by Jean-Fran\c{c}ois
Cardoso. You have to put the code under this directory "ica".
usage is
function [Y1,Y2] = ica_f(X,NFFT,FS,OVERLAP,N)
where
X : is a ".wav" data (You should use 16kHz sampling rate).
NFFT : number of the FFT points.
FS : sampling rate (it should be 16000 Again..)
OVERLAP : overlap of the window function, if sampling rate 16kHz, it
should be NFFT-20.
N : number of the matrixes to be simultaneous diagonalized. 40
is my recommendation.
I use this ica_f in the following way.
>> X=wavread('***.wav')
>> [Y1,Y2] = ica_f(X,512,16000,492,40);
X_linear.wav, X_room.wav are for test use. X_linear is an instantaneous
mixture, and X_room is a convolutive mixture.
Shiro Ikeda, shiro@ikeda.cc
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