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.