Hi @voltist, It’s not intuitive because it’s hard to see, but in the upper left of the wow and flutter tool there’s a help button that will open in your browser a small HTML file of help on the tool. I’ve pasted that below. The two-sigma value is twice the standard deviation.

Wiki notes:

“Physical scientists often use the term root mean square as a synonym for standard deviation when it can be assumed the input signal has zero mean, that is, referring to the square root of the mean squared deviation of a signal from a given baseline or fit.[8][9] This is useful for electrical engineers in calculating the “AC only” RMS of a signal. Standard deviation being the RMS of a signal’s variation about the mean, rather than about 0, the DC component is removed (that is, RMS(signal) = stdev(signal) if the mean signal is 0).”

So, let’s say you are looking at a 10 Hz carrier that is modulated by a 1 Hz low frequency modulator. That would give a resultant that spans from 9 to 11 Hz. In the W&F tool, that would show as a sine centered on 10 Hz with peaks +/-1 Hz. The RMS of that would be ~10. But the standard deviation would give the AC only, which would be 0.707.

In the picture pasted below, you can see this math in Excel. The second column is a sin() with an amplitude of 1 added to a fixed value of 10. On the right, you’ll see that value squared. At the bottom of the right hand column, you can see the mean and root and we arrive at an RMS of 10 as expected. And at the bottom of the second column you can see the standard deviation is 0.707, which is what we’d expect for a sin with an amplitude of 1.

We’ll add a way to see max and min, as that is good info to have that’s hard to pull from a plot. But roughly, just take a look at the stddev value and divide it in half and that is your RMS (with the carrier removed) if the waveform is a sine.

(Below is pasted from html help)

## Wow and Flutter

This visualizer will measure the unweighted wow and flutter of a playback device. The range of tones expected falls between 1 KHz to 3 KHz, but other frequencies will work.

The Sample Length determines the lower bound low-frequency variations that can be detected. A good starting point is around 32K to 64K size FFT and a 48K sample rate.

The input signal needs to be fairly strong and free of noise. A good target is around -30 dBV or higher, with the attenuator off. Noisy signals will be more prone to variation.

## Measurement Computation

The incoming signal is demodulated, and a graph is displayed showing frequency versus time. Ideally there would be no variation in playback and the line would be horizontal with no variation or noise.

The indicated Frequency is the frequency averaged over the entire sample interval (eg 32K points).

In computing the 2-Sigma value, the demodulated signal standard deviation is computed. The displayed value is the peak-to-peak detected and twice the computed sigma, in accordance with AES6-2008. Currently, however, the reading isn’t weighted.