I don't think you quite have the hang of the conversations system

, but in any case I have added cross-correlation averaging for the next

REW beta build, probably this weekend. It uses the reference channel as the second input. When the noise floors of the inputs are dominated by uncorrelated zero mean noise sources the benefit is larger than might be expected from 5*log10(n), but trying it out on a few interfaces I have lying about suggests the gain is more typically much smaller than one might expect.

Good morning, John

Yes I am new to ASR and I do not master

not yet the conversation system.

Anyway, thank you for watching, on behalf of everyone.

There are people who will be happy.

regarding the 5*log(n) noise reduction formula

it is true (in measurements) for noise differences relatively

low between DUT and ADC. As what we can see

in our measurements.

Example DAC noise -125dbFS and ADC noise at -120dbFS

I left several graphics throughout the discussion

regarding this noise reduction.

I republish here the noise reduction by correlation

between a 1000Hz signal and a 1000Hz + 2000Hz signal

all frequencies having the same amplitude.

The 2000 Hz signal is considered noise.

The first correlation drops the 2000HZ more

of 100dB.

If we add other correlations we enter the

reduction of 5*log(n) .

This first correlation varies in noise reduction

with the weighting window, but I don't know why.

You can try it with the window

flat top HFT248D which I consider to be the most precise in amplitude

and which has the lowest noise floor I know.

the equation is in my FFT calculation script.

And then make the difference with a Blackman 7.

I think the difference in amplitude precision on windows

modifies the result of the correlation, but it must

prove .

Apologies regarding the frequency scale presented here

I had an octave Bug, the 2 frequencies are 1000Hz and 2000Hz.

As I said throughout this discussion, it is still necessary

take the equation 5*log(n) with watchful eyes.

In any case I repeat my thanks for watching.

Sincerely