Standard deviation of spectral flatness -- so what am I measuring, conceptually? In my never-ending quest to identify snores, I've found that "spectral flatness" seems to be a fair measure of signal "quality".  

I'm computing spectral flatness as the geometric mean of the FFT power $(R*2 + I*2)$ data points divided by the arithmetic mean of the same points.

I then (a little twist here) am computing the running (over 50 frames) arithmetic mean and standard deviation of the spectral flatness and computing a "normalized" standard deviation as the running standard deviation divided by the running mean.

For my samples I find that this metric is greater than about $0.2$ (ranging up to $0.5$ or so) when audio is "good" (ie, I have reliable tracking of the breathing/snoring sounds of a sleeping subject) and it generally slips down below $0.2$ when audio is "in the mud".  (I can improve on this discrimination somewhat by using a threshold that moves with other factors, but that's presumably a different topic.)  I also observe that the measure goes over $1.0$ when there's substantial background noise (eg, someone enters the room and rustles about).

So, my basic question is:  Is there a name (beyond "normalized standard deviation of spectral flatness") for what I'm measuring, and can anyone offer a conceptual explanation of what the metric "means"?

(I've tried a dozen other metrics for signal "quality", and this one seems to be the best to date.)

Added:  I probably should admit that I don't have a particularly good conceptual handle on what simple spectral flatness is measuring (just the Wikipedia article), so any further explanation of that would be appreciated.
Since you are interested in the "flatness" of your spectrum, in fact, you are interested in how close your signal is to a white noise (that by definition has a flat spectrum + random phases). If you step back, one measure would be the "distance" of your observation to the white noise reference.

The obvious measure in terms of information theory is Kullback-Leibler divergence. You do not need to understand every part of it, but it measures in bits (if you use log base 2) the distance between both distributions.

The good thing in your case is that your reference is flat, so that what remains is the entropy of your spectrum. There are many existing implementation (for instance in scipy).

Note that you are still on the safe side: if your distribution is approximately gaussian, both measures (entropy and std) will be proportional. The entropy is however more general and more principled. As an extension, you will be able to generalize to other types of noises (1/f for instance).