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% Demonstration code for "Independent component analysis: A Tutorial
Introduction"
% JV Stone, MIT Press, September 2004.
% Copyright: 2005, JV Stone, Psychology Department, Sheffield University,
Sheffield, England.
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% Basic Bell-Sejnowski ICA algorithm demonstrated on 2 speech signals.
% The default value of each parameter is given in [] brackets.
% [0] Set to 1 to hear signals.
listen=0; % set to 1 if have audio.
% [1] Set random number seed.
seed=9; rand('seed',seed); randn('seed',seed);
% [2] M = number of source signals and signal mixtures.
M = 2;
% [1e4] N = number of data points per signal.
N = 1e4;
% Load data, each of M=2 columns contains a different source signal.
% Each column has N rows (signal values).
% Load standard matlab sounds (from MatLab's datafun directory)
% Set variance of each source to unity.
load chirp; s1=y(1:N); s1=s1/std(s1);
load gong; s2=y(1:N); s2=s2/std(s2);
% Combine sources into vector variable s.
s=[s1,s2];
% Make new mixing matrix.
A=randn(M,M);
% Listen to speech signals ...
% [10000] Fs Sample rate of speech.
Fs=10000;
if listen soundsc(s(:,1),Fs); soundsc(s(:,2),Fs);end;
% Plot histogram of each source signal -
