I'm trying to convert Matlab code to C++ or C, but it has some errors. Can someone help me with this?
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%function [e,w]=lmsBasic(M,x,d);
% 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];
for j = 1:length(mus)
for i = 1:100
%%INITIALIZE VALUES%%
% generate input signal
M=128; %buffer size (num filter weights)
%=audioread('Airplane Sound Effect.m4a'); %input signal
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');
pause(17);
soundsc(x,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')
5 个评论
回答(1 个)
Walter Roberson
2021-3-1
编辑:Walter Roberson
2021-3-1
My adjusted version of the code passes compilation, and produces graphs. However I can demonstrate that the algorithm is wrong. And besides that, it produces NaN values.
function lmsBasic()
%
% 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
M=128; %buffer size (num filter weights)
mus = [0.01 0.05 0.1 0.5 1 2];% 0.5 1];
fs=8000; %number of samples of the input signal
Nrand = 20000;
Niter = 100;
Nmus = length(mus);
mu = zeros(Nrand,1);
e = zeros(Nrand,1);
eall = zeros(Nmus, Nrand);
for j = 1:Nmus
emeans = zeros(Nrand, 1);
for i = 1:Niter
%%INITIALIZE VALUES%%
% generate input signal
x=randn(Nrand,1); %input signal
x=x/max(x); %sample rate
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(Nrand,1);
%Make sure that x and d are column vectors
x=x(:);
d=d(:);
%LMS
for n=M:Nrand
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
end
e=e(:);
emeans = emean(:)+e;
end
emeans=(emeans)/Niter;
if sum(isinf(emeans))>0
emeans(~isinf(emeans))=1e3;
eall(j,:)=emeans;
elseif max(emeans)>1e4
for l = 1:length(emeans)
if abs(emeans(l)) > 1e3
emeans(l)=1e3;
end
end
eall(j,:)=emeans;
else
[eall(j,:),q]= envelope(abs(emeans),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');
%}
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