How to detect "noisiness" of sound wave? Some phonemes like "ssss" are basically white noise. How would you determine which parts of a wave are white noise?
From frequency analysis the white noise will have no tones so just using this would suggest it would be indistinguishable from no noise at all! (multiplying it by a sine wave would cancel out at all frequencies). 
How do our ears hear white noise? How does it know it's not just a high frequency?
Is there a way to detect, say the entropy of a sound wave to determine which bits are white noise?
I want to write some software that detects things like the phoneme "ssss" so it needs to work out which bits of a wave are white noise.
Edit
I kind of get that you need to do an FFT to find the noise. So say $f(x)$ is a digital wave for x=0...1000 bytes.
Is there some kind of functional such as $F[f]$ which will give a number between 0..1 to say how close to white noise the signal is?
From the top of my head maybe something like:
$$F[f] = 1 - \frac{1}{1000^2}\int_0^{1000} \int_0^{1000} |f(x)-f( (x+a)_{mod 1000})|^2 da dx$$
but this is a double integral an would take a long time. My idea is that white noise would have no repetition so the integrand would never be zero. Just a guess. Don't think it would work in practice.  Is there a simpler formula perhaps with just one integral or  a sum?
 A: The definition of (discrete) white noise includes the property that the autocovariance is zero for all nonzero time lags. (For zero time lag, it is the variance.) Conceptually, white noise is completely uncorrelated with itself. This property leads to the uniform density in Fourier space, but the autocovariance is easy to estimate from a record of fixed length. Nearly-white noise should have a very narrow peak in the autocovariance at zero lag.
A more rigorous approach, which would give you a less subjective measure of the "whiteness" of your process, would be to fit the time series to an autoregressive moving-average (ARMA) model. R, Matlab, and Mathematica all have packages for this, and I'm sure you could find open-source versions. The closer the process constants are to zero, the closer your series is to white noise.
However: To my mind, this isn't really an acoustics question so much as a time series analysis question. You may get better answers if you migrate over to the Signal Processing or Mathematics Stack Exchange sites.
A: Use an FFT to characterize the sound. With a pure tone, there will be lots of strength in one or two adjacent bins, with all others having very little signal. With white noise, every bin will have strength, as there will be elements of every frequency present. Yes, the bin strengths will be noisy, and you'll have to deal with that, but a little bit of statistics should go a long way.
