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ICAforsound

于 2006-08-23 发布 文件大小:1638KB
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  Matlab环境下的基于独立分量分析(ICA)算法的声音混合与分离仿真程序(Matlab environment based on the independent component analysis (ICA) is the voice mixed with the separation of simulation program)

文件列表:

dragen.wav
man.wav
music.wav
soundOK.m

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  • 1052
    MATLAB源码,preprocessing.m: preprocess the picture including:Wiener filter; meddle value filter, change a gray picture to a binary one and then get the skeleton. centralizing.m: input the source picture output the core point of the fingerprint. samfun.m: generate the sample pattern automatically, which is used to sample the fingerprint. gabor2d_sub: get gabor filter. varpic.m: calculate variance picture(MATLAB source, preprocessing.m : preprocess the picture including : Wiener filter; meddle value filter, change a gray picture to a binary one and then get the skeleton. centralizing.m : input the source picture output the core point o f the fingerprint. samfun.m : generate the sample pattern automatically, which is used to sample the fingerprint. gabor2 d_sub : get gabor filter. varpic.m : calculate variance picture)
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    说明:  盲源分离FastICA、matalab程序,(FAST KERNEL ICA | |--------------------| Version 1.0- February 2007 Copyright 2007 Stefanie Jegelka, Hao Shen, Arthur Gretton This package contains a Matlab implementation of the Fast Kernel ICA algorithm as described in [1]. Kernel ICA is based on minimizing a kernel measure of statistical independence, namely the Hilbert-Schmidt norm of the covariance operator in feature space (see [3]: this is called HSIC). Given an (n x m) matrix W of n samples from m mixed sources, the goal is to find a demixing matrix X such that the dependence between the estimated unmixed sources X *W is minimal. FastKICA uses an approximate Newton method to perfom this optimization. For more information on the algorithm, read [1], and for more information on HSIC, refer to [3]. The functions chol_gauss and amariD are taken from and based on, respectively, code from Francis Bach (available at http://cmm.ensmp.fr/~bach/kernel-ica/index.htm). The derivative is com)
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