Christopher Brown

It's me again ...

Finally, I found the problem in your code!

First, I was wrong with my speculation that the r-squared formula would be off by a division by "n".
The formula is OK (it's just the squared version of Pearson's product-moment correlation coefficient).
The following regression is also correct.

However, there is an error when calculating the intersection between the regression line with the -60 dB line.

Your code is:

rt = round(abs(60/slope(1))-yintercept);

This works if yintercept is (very close to) zero (as it is in the nice picture above. But in the general case (and in my case), yintercept is a non-negligible negative value and the resulting reverberation time is wrong.

The correct line would be:

rt = -(yintercept + 60)/slope;

As I said, the regression is correct, however it could be written a little more compactly like this:

xx = xtime(1:stop);
yy = y(1:stop);
slope = (mean(xx.*yy) - mean(xx)*mean(yy))/(mean(xx.^2) - mean(xx)^2);
yintercept = mean(yy) - slope * mean(xx);

That's it about my first point, I'm still searching for an answer regarding my second question.

cheers,
Matthias

very efficient code

There are couple issues with this MLS sequence generator. The primitive polynomial list has a few errors (lines 29-180). This list is not necessary given that Matlab has a built in function to generate these (gfprimdf.m) For example, the 7th order MLS
sequence output is incorrect. Given the use of the tap list, this code is slightly inefficient.