Final-Code.txt

a='D:\Karan-ICT\SS_Project\strings.wav';
[y,Fs] = audioread(a);
info = audioinfo(a);

 Mean=0.05;
 Var=0.005;
 z=imnoise(y,'Gaussian',Mean,Var);
 b='D:\Karan-ICT\SS_Project\stringsnoise.wav';
audiowrite(b,z,Fs);

mtlb_noisy = y;
noise = z;
% Define Adaptive Filter Parameters
filterLength = 1000;
weights = zeros(1,filterLength);
step_size = 0.004;
% Initialize Filter's Operational inputs
output = zeros(1,length(mtlb_noisy));
err = zeros(1,length(mtlb_noisy));
input = zeros(1,filterLength);
% For Loop to run through the data and filter out noise
for n = 1: length(mtlb_noisy),
%Get input vector to filter
for k= 1:filterLength
if ((n-k)>0)
input(k) = noise(n-k+1);
end
end
output(n) = weights * input'; %Output of Adaptive Filter
err(n) = mtlb_noisy(n) - output(n); %Error Computation
weights = weights + step_size * err(n) * input; %Weights Updating
end
yClean = err;

 c='D:\Karan-ICT\SS_Project\stringsexpected.wav';
audiowrite(c,output,Fs);


subplot(3,1,1);
plot(y);            % Original Signal
title('Original Signal');
xlabel('Time (s)');
ylabel ('Amplitude');

subplot(3,1,2);
plot(z);          % Noisy output
title('Noisy signal');
xlabel('Time (s)');
ylabel ('Amplitude');

subplot(3,1,3);
plot(output);                  % Filtered output
title('Filtered Signal');
xlabel('Time (s)');
ylabel ('Amplitude');