Can't play sound q

clc
clear
close all
%function [e,w]=lmsBasic(M,x,d,h);
%
% Input arguments:
% M = learned filter length, dim 1x1
% x = input signal, dim Nx1
% d = desired signal, dim Nx1
% h = input coefficients  (coeff of filter we
%       are trying to learn. filter
%       that turns d into u)
%
% Output arguments:
% e = estimation error, dim Nx1
% w = final filter coefficients, dim Mx1
mus = [0.01 0.05 0.1 0.5 1 2];% 0.5 1];
[x, fs] = audioread('xd.mp4');
orgin = x(:, 1);
for j = 1:length(mus)
    for i = 1:100
        %%INITIALIZE VALUES%%
        % generate input signal
        M=128; %buffer size (num filter weights)
        %x=randn(20000,1); %input signal
        %[x, fs] = audioread('xd.mp4');
        %orgin = x(:, 1);
        x=x/max(x); %sample rate
        %fs=8000; %number of samples of the input signal
        N=length(x); %length of input signal
        muOG = mus(j);
        
        % generate known filter coefficients
        Pz=(0.5*[0:127]).^2;  %linear coefficients
        %Pz=randn(128,1); %random coefficients
        ylim = max(Pz)*1.20;
        ymin = min(Pz)-.2*max(Pz);
        % generate filtered input signal == desired signal
        d=conv(Pz,x); %input signal filtered by known filter Pz (primary path)
        
        
        %% LMS FOR MAIN ANC %%
        
        %initalize Wz filter values
        Wz=zeros(M,1);
        emean=zeros(N,1);
        %Make sure that x and d are column vectors
        x=x(:);
        d=d(:);
        %LMS
        
        for n=M:N
            xvec=x(n:-1:n-M+1); %input has to be in reverse orxer
            %adaptively update mu
            mu(n) = muOG;
            e(n)=d(n)-Wz'*xvec; %update error
            Wz=Wz+mu(n)*xvec*(e(n)); %update filter coefficient
            
            %visualize learned filter in realtime
            %         plot(Pz)
            %         hold on
            %         plot(Wz)
            %         axis([0 inf ymin ylim])
            %         title(sprintf('n=%f time=%fs error = %f mu=%f',n-M, (n-M)/fs, e(n), mu(n-M+1)))
            %         hold off
            %         legend('Input coefficients','Learned Coefficients')
            %         drawnow;
        end
        e=e(:);
        emean = (emean(:)+e);
    end
    emean=(emean)/i;
    if sum(isinf(emean))>0
        emean(~isinf(emean))=1e3;
        eall(j,:)=emean;
    elseif max(emean)>1e4
        for l = 1:length(emean)
            if abs(emean(l)) > 1e3
                emean(l)=1e3;
            end
        end
        eall(j,:)=emean;
    else
        [eall(j,:),q]=(envelope(abs(emean),150,'peaks'));
    end
end
figure
for i = 1:length(mus)
    plot(10*log10(abs(eall(i,:))))
    hold on
end
title('Convergence Time in Cycles')
ylabel('Error (dB)');
xlabel('Cycles');
hleg=legend('0.01','0.05','0.1','0.5','1.0', '2.0');
htitle=get(hleg,'Title');
set(htitle,'String','mu');
soundsc(orgin, fs);
pause(20);
soundsc(x,fs);
pause(20);
soundsc(q, fs);
pause(20);
soundsc(e, fs);
% %% PLOT RESULTS %%
% figure
% subplot(2,1,1)
% plot(e)
% title('Convergence Time in Cycles')
% ylabel('Amplitude');
% xlabel('Cycles');
% legend('Error');
% subplot(2,1,2)
% stem(Pz)
% hold on
% stem(Wz, 'r*')
% title('Input Coefficients vs Learned Coefficients')
% ylabel('Amplitude');
% xlabel('Numbering of filter tap');
% legend('Input Coefficients', 'learned coefficients')

Im using this LMS algorithm to test ANC.

I simulated the sound cancelling by typing "soundsc(x+q, fs);" and saw a decrease in dB.

But when I try to listen to q seperately by typung "soundsc(q, fs);" nothing happened.

Is there a way that I can play q and fully listen to it?

Also I'm not sure if q is the anti-noise being produced.

I can't find the anti-noise at all.

Is there an anti-noise even being produced, and if so, how can I play it?

Thank you for your time.

Answer 1

[Rishav]

Hi Sean,

I understand that you are unsure about the variable 'q' and what it does. Moreover, you want to listen to the Anti-noise signal.

'q' is not related to the anti-noise signal or the active noise cancellation (ANC) process directly. Instead, it is part of the process to visualize the convergence of the error signal over time for different values of the step size 'mu'. The variable 'q' ends up holding the upper envelope of the last calculated mean error signal 'emean' processed in your loop for different 'mu' values.

To listen to the Anti-Noise signal, you would need to explicitly store the output of your adaptive filter (anti-noise signal) in a separate variable during your loop.

Please follow the steps below to modify the loop to include this:

Before the loop, initialize an array to store the anti-noise signal:

antiNoise = zeros(N,1); % Initialize the anti-noise signal storage

Inside your LMS loop, update this 'antiNoise' array with the current output of the adaptive filter:

for n=M:N
    xvec=x(n:-1:n-M+1); %input has to be in reverse order
    % adaptively update mu
    mu(n) = muOG;
    e(n)=d(n)-Wz'*xvec; %update error
    antiNoise(n) = Wz'*xvec; % Store the current output of the adaptive filter
    Wz=Wz+mu(n)*xvec*(e(n)); %update filter coefficient
end

After the loop, you can then play back the 'antiNoise' signal to listen to it:

soundsc(antiNoise, fs);