Definition: To say that is a white noise means merely that successive samples are uncorrelated:
where denotes the expected value of (a function of the random variables ).
In other words, the autocorrelation function of white noise is an impulse at lag 0. Since the power spectral density is the Fourier transform of the autocorrelation function, the PSD of white noise is a constant. Therefore, all frequency components are equally present--hence the name ``white'' in analogy with white light (which consists of all colors in equal amounts).
An example of a digital white noise generator is the sum of a pair of dice minus 7. We must subtract 7 from the sum to make it zero mean. (A nonzero mean can be regarded as a deterministic component at dc, and is thus excluded from any pure noise signal for our purposes.) For each roll of the dice, a number between and is generated. The numbers are distributed binomially between and , but this has nothing to do with the whiteness of the number sequence generated by successive rolls of the dice. The value of a single die minus would also generate a white noise sequence, this time between and and distributed with equal probability over the six numbers
To obtain a white noise sequence, all that matters is that the dice are sufficiently well shaken between rolls so that successive rolls produce independent random numbers.C.4
It can be shown that independent zero-mean random numbers are also uncorrelated, since, referring to (C.26),
For Gaussian distributed random numbers, being uncorrelated also implies independence . For related discussion illustrations, see §6.3.
As mentioned in §6.12, the pwelch function in Matlab and Octave offer ``confidence intervals'' for an estimated power spectral density (PSD). A confidence interval encloses the true value with probability (the confidence level). For example, if , then the confidence level is .
This section gives a first discussion of ``estimator variance,'' particularly the variance of sample means and sample variances for stationary stochastic processes.
The simplest case to study first is the sample mean:
Here we have defined the sample mean at time as the average of the successive samples up to time --a ``running average''. The true mean is assumed to be the average over any infinite number of samples such as
Now assume , and let denote the variance of the process , i.e.,
Then the variance of our sample-mean estimator can be calculated as follows:
We have derived that the variance of the -sample running average of a white-noise sequence is given by , where denotes the variance of . We found that the variance is inversely proportional to the number of samples used to form the estimate. This is how averaging reduces variance in general: When averaging independent (or merely uncorrelated) random variables, the variance of the average is proportional to the variance of each individual random variable divided by .
Consider now the sample variance estimator
where the mean is assumed to be , and denotes the unbiased sample autocorrelation of based on the samples leading up to and including time . Since is unbiased, . The variance of this estimator is then given by
by the independence of and , and when , the fourth moment is given by . More generally, we can simply label the th moment of as , where corresponds to the mean, corresponds to the variance (when the mean is zero), etc.
When is assumed to be Gaussian white noise, we have
so that the variance of our estimator for the variance of Gaussian white noise is
Again we see that the variance of the estimator declines as .
The same basic analysis as above can be used to estimate the variance of the sample autocorrelation estimates for each lag, and/or the variance of the power spectral density estimate at each frequency.
Gaussian Window and Transform