Guys how can i change the vector length in column number 50 (figure 2) of my code below? i have this error "Error using plot. Vectors must be the same length"

clc

%% Block Adaptation using window over a signal %%

rootdirectory = 'E:\';

[input,fs] = audioread('bsp1.wav'); % Read audio file

t = (1:length(input))/fs;

width = 300; % the width of the window

shift = width/2; % the shift of the Window

Lp = 2; % LP

window = hamming(width); % Hamming window

L = length(input); % size of input vector

Window_step_no = round((L-width)/shift); % Number of steps based on the window shift times throught the input full length

%%Reconstruct the input signal based on the framming for Lp=2

count = 1;

for i=1:Window_step_no

y(i,:) = input(count:count+width-1).*window; % multiply the signal by the hamming window and save the row values for i steps

count = count+shift; % window interval shift

a = lpc(y(i,:),Lp); % calculate the linear prediction Coeficient for each step

est(i,:) = filter([0 -a(2:end)],1,y(i,:)); % reconstruct or predict the signal for each step

err(i,:) = y(i,:)-est(i,:); % Error signal for each step or window

end

% Reshape the prediction signal by using vercat

for i=1:Window_step_no

s(i,:) = est(i,1:shift); % make a new matrix for the predicted signal

end

%Modifiying the step size to an even for the vercat function

chk = mod(Window_step_no,2);

if chk == 0

stepn = Window_step_no; % if even

else

stepn = Window_step_no-1; % if odd

count = 1;

end

for i=1:2:(stepn)

r(count:count+width-1,1) = vertcat(s(i,:).',s(i+1,:).'); % applying vercat to reshape the predicted signal

count = count+width;

end

figure(1)

plot(t,input)

xlabel('t in s')

ylabel('Signal amplitude')

title('Original Signal')

audiowrite('E:\in.wav',input,fs); % Write audio file to .wav file

figure(2)

plot(t, r)

xlabel('t in s')

ylabel('Signal amplitude')

title('Reconstructed signal with window method')

audiowrite('E:\r.wav',r,fs); % Write audio file to .wav file

errorsignal=input-r;

figure(3)

plot(t,errorsignal)

xlabel('t in s')

ylabel('Signal amplitude')

title('Reconstructed signal with window method')

audiowrite('E:\errorsignall.wav',errorsignal,fs); % Write audio file to .wav file

%% Quantize error signal

LPC_Coeff=lpc(input,Lp);

E_6 = quant(errorsignal,(2^(-6+1))); % Quantize error for 6 bit

E_7 = quant(errorsignal,(2^(-7+1))); % Quantize error for 7 bit

E_8 = quant(errorsignal,(2^(-8+1))); % Quantize error for 8 bit

x_recon6=filter([0 -LPC_Coeff(2:end)],1,E_6); %Reconstructed signal from 6bit quantized error signal

figure(4)

plot(t,x_recon6,'b');

xlabel('t in s')

ylabel('Signal amplitude')

title('Reconstructed signal from 6bit quantized error signal')

audiowrite('E:\Reconstructed_6bitnew.wav',x_recon6,fs);

x_recon7 = filter([0 -LPC_Coeff(2:end)],1, E_7); %Reconstructed signal from 7bit quantized error signal

figure(5)

plot(t,x_recon7,'b');

xlabel('t in s')

ylabel('Signal amplitude')

title('Reconstructed signal from 7bit quantized error signal')

audiowrite('E:\Reconstructed_7bitnew.wav',x_recon7,fs);

x_recon8=filter([0 -LPC_Coeff(2:end)],1,E_8); %Reconstructed signal from 8bit quantized error signal

figure(6)

plot(t,x_recon8,'b');

xlabel('t in s')

ylabel('Signal amplitude')

title('Reconstructed signal from 8bit quantized error signal')

audiowrite('E:\Reconstructed_8bitnew.wav',x_recon8,fs);

%% Variance of the original signal and error signal

varx = var(input);

vare = var(errorsignal);

m = 2;

for n=4:4:20

count = 1;

for i=1:Window_step_no

y(i,:) = input(count:count+width-1).*window;

count = count+shift;

a = lpc(y(i,:),n); % Linear prediction for different linear predictor values n

est(i,:) = filter([0 -a(2:end)],1,y(i,:));

err(i,:) = y(i,:)-est(i,:);

end

for i=1:Window_step_no

s(i,:) = est(i,1:shift);

end

count = 1;

for i=1:2:(stepn)

r(count:count+width-1,1) = vertcat(s(i,:).',s(i+1,:).');

count = count+width;

end

errorsignal1=input-r;

varen = var(errorsignal1); %error signal variance for each n value

vare = [vare varen];

m = [m n];

end

%% Prediction gain for all LP Predictors

for i=1:6

G(i)=10*(log(((varx)/(vare(i)))));

end

figure(7)

stem(m,G)

title('The new Prediction gain using Framming technique')

xlabel('No. predictors')

ylabel('prediction gain ')

G1=[ 39.5490 40.5707 43.1145 43.4124 44.0755 44.1995];

figure(8);

subplot(2,1,1)

stem(m,G1);

xlabel('No. Predictors')

ylabel('Prediction Gain')

title('Prediction gain for all LP Predictors')

hold on

subplot(2,1,2)

xlabel('No. predictors')

stem(m,G);

ylabel('prediction gain ')

title('The new Prediction gain using Framming technique')

Answer 1

[Jon]

I may have missed something, but it looks like the first place that your variable r is assigned is the line

r(count:count+width-1,1) = vertcat(s(i,:).',s(i+1,:).'); % applying vercat to reshape the predicted signal

If that is the case then, assuming at this point count is greater than 1, your variable r(1), r(2), ... r(count-1) , will all have values of zero, and perhaps the length of r will also not be what you expect and is leading to problems with the lengths not matching in your plot 2. Is this what you intended? It is generally better to preallocate arrays (assign them to a vector, or matrix of zeros of the appropriate size) before entering a loop.

To further understand what your code is doing you will need to provide a self contained example that demonstrates the problem or attach any needed data files, e.g. bsp.wav

Also some MATLAB answers pointers for future reference. Please put a short title which generally captures the nature of your problem and then put the details in the body of the question. Also please use the code button to include code and have it nicely formatted and easy to copy.

[Jon]

Stepping through with the debug tool (I recommend learning how to use this if you don't already know) I find that r has 235500 elements and t has 235736. So the two vectors definitely are not the same size.

What size would you expect r to be?

You will have to look into the details of your calculations and assignments to see why r does not have the expected number of elements.