Tuner Upper Removal Demonstration: Difference between revisions

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  % Demonstration of LMS algorithm for noise cancellation.
  <nowiki>
% Rich Kozick, Spring 1997
% Demonstration of LMS algorithm for noise cancellation.  Tuner upper problem.
% Rob Frohne's  modifications for Macintosh 2000.  
% Rich Kozick, Spring 1997
% Desired signal
% Rob Frohne's  modifications for Macintosh 2000
clear all
% and Linux 2006 or so.
Totaltime=1;
 
<nowiki>speak('Hit a key and speak the signal.');</nowiki>
 
pause;
clear all
[st, Fs] = recordsound(Totaltime, 22050, 1);
Fs=8000; % 8 Khz sampling for Linux.
s = st';
T0 = 2; % 2 seconds
Ls = length(s);  
 
% Interference + random noise
system("espeak 'After hitting enter in the command window, speak the signal.'");
speak('Hit a key and make the noise!');
st = record(T0,Fs);
pause;
%st=wavread('Hello.wav');
[nt,Fs] = recordsound(Totaltime, 22050, 1);
st=st(:,1);
n = nt';
 
%Sign = 0.01;
system("espeak 'Here is the signal.'")
%Dn=20; % Delay of the noise that appears in y.
soundsc(st);
%n = n(1:Ls) + Sign*randn(Ls,1);
 
%an = [0 .01 -.5 1 -.5 .1 .01 0];
Ls = length(st);
an = 4*[0 0 0 0 0 0 0 0 0 0 0 0 0 0 .5 1 .5];
T0=length(st)/Fs;
bn = [1];
%  Make the tuner upper noise or even several at the same time.
%sys = tf(an,bn,1/Fs);
t=0:1/Fs:T0-1/Fs;
%bode(sys);
n = 10*(sin(2*pi*100*pi*t) + cos(2*pi*600*t) + 1.5*sin(2*pi*850*t));
%figure(1);
 
nf = filter(an,bn,n);
% Add him or them to the desired signal.
%nf = [nf(Dn:(Ls)); zeros(Dn-1,1)] + Sign*randn(Ls,1); 
x = st' + n;
y = s + nf;  
 
Sigx = 0.01;
system("espeak 'Here is the noisy signal.'")
bx = [1];      % bx and ax are filtering on n to produce x
soundsc(x,Fs);
ax = [1];
 
Dx = 1;        % Delay of n that appears in x
N = 64;        % Length of adaptive filter
x = filter(bx, ax, n);%x = [x(Dx:(Ls)); zeros(Dx-1,1)] + Sigx*randn(Ls,1);
 
%x = n + Sigx*randn(Ls,1); 
% LMS algorithm for adaptive noise cancellation
speak('Here is the noisy signal.')
 
soundsc(y,Fs);  
h = ones(N,1);
N = 20;        % Length of adaptive filter  
mu = 0.1;
% LMS algorithm for adaptive noise cancellation  
for k=N:Ls
h = zeros(N,1);
  xk = x(k:-1:(k-N+1));
mu = 1/(10*N*var(x));
  y(k) = h'*xk';
%mu = 1.0;
  e(k) = y(k);
for k=N:Ls     xk = x(k:-1:(k-N+1));     nhat(k) = h'*xk;     e(k) = - y(k) + nhat(k);     h = h - mu*e(k)*xk;%/(xk'*xk);
  h = h - 2*mu*e(k)*xk'/(xk*xk');
end  
end
 
% The signal estimate is in the vector e
% The signal estimate is in the vector e
speak('Here is the cleaned signal.');
system("espeak 'Here is a scaled version of the tail of the cleaned signal.'");
soundsc(e,Fs);
skip =1000;
soundsc(e(skip:length(e)),Fs);
</nowiki>

Latest revision as of 14:43, 6 December 2015

% Demonstration of LMS algorithm for noise cancellation.  Tuner upper problem.
% Rich Kozick, Spring 1997
% Rob Frohne's  modifications for Macintosh 2000
% and Linux 2006 or so.


clear all
Fs=8000; % 8 Khz sampling for Linux.
T0 = 2;  % 2 seconds

system("espeak 'After hitting enter in the command window, speak the signal.'");
st = record(T0,Fs);
%st=wavread('Hello.wav');
st=st(:,1);

system("espeak 'Here is the signal.'")
soundsc(st);

Ls = length(st);
T0=length(st)/Fs;
%  Make the tuner upper noise or even several at the same time.
t=0:1/Fs:T0-1/Fs;
n =  10*(sin(2*pi*100*pi*t) + cos(2*pi*600*t) + 1.5*sin(2*pi*850*t));

% Add him or them to the desired signal.
x = st' + n;

system("espeak 'Here is the noisy signal.'")
soundsc(x,Fs);

N = 64;         % Length of adaptive filter

% LMS algorithm for adaptive noise cancellation

h = ones(N,1);
mu = 0.1;
for k=N:Ls
  xk = x(k:-1:(k-N+1));
  y(k) = h'*xk';
  e(k) = y(k);
  h = h - 2*mu*e(k)*xk'/(xk*xk');
end

% The signal estimate is in the vector e
system("espeak 'Here is a scaled version of the tail of the cleaned signal.'");
skip =1000;
soundsc(e(skip:length(e)),Fs);
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