Similarity of two .wav files - unsure about the returned error.

Hi, I'm wanting to use correlation to see how similar an original sound and one I synthesized from it are. I was advised to use the resample function however, due to lack of knowledge in MATLAB I'm looking for a little help to gain my metric as I'm just not understanding things. I have attached the audio files if anyone could please help me out a little?

clear, clc, close all
% get a section of the sound file
[x, fs] = audioread('Machine_Gun.wav');   % load an audio file
x = x(:, 1);                        % get the first channel
N = length(x);                      % signal length
t = (0:N-1)/fs;                     % time vector
[y, fs] = audioread('Dry_Synthesized_Machine_Gun.wav');   % load an audio file
y = y(:, 1);                        % get the first channel
n = length(y);                      % signal length
T = (0:n-1)/fs;
data = [x y];
correlation = corrcoef(data);

This was my failed attempt and i got the below error message:

Error using horzcat

Dimensions of arrays being concatenated are not consistent.

Error in test (line 12)

data = [x y];

Answer 1

[Jan]

Error using horzcat
Dimensions of arrays being concatenated are not consistent.
data = [x y];

This means the x and y have a different number of elements. Then a horizontal concatenation cannot work, because matrices must be rectangular.

You have mentioned resample already, which allows to adjust the sizes of the vectors. interp1 would be fine also. But this would change the frequency of the signal. Maybe cropping or appending zeros is better:

% get a section of the sound file
[x, fsx] = audioread('Machine_Gun.wav');   % load an audio file
[y, fsy] = audioread('Dry_Synthesized_Machine_Gun.wav');   % load an audio file
x = x(:, 1);      % get the first channel
y = y(:, 1);      % get the first channel

nx = length(x);   % signal length
ny = length(y);   % signal length

yPad  = y;
yCrop = y;
xPad  = x;
xCrop = x;
if nx > ny
    yPad(nx) = 0;
    xCrop    = xCrop(1:ny);
elseif nx < ny
    xPad(ny) = 0;
    yCrop    = yCrop(1:nx);
end

c1 = corrcoef(xPad, yPad);
c2 = corrcoef(xCrop, yCrop);

[Taylor Gray]

Thank you very much for the help this gives me the metric I needed, just one more question and apologies for the lack of knowledge but the result is

1 -0.0735995787617834

-0.0735995787617834 1

When I was told to look at this function for similarity I was told anwhere between 0.6 to 1 would be a good result, does my result of -0.0735995787617834 mean it is not similar at all?

[Jan]

Which "result" is this?

[Taylor Gray]

c1 & c2