### What is Noise?

Consider the spectrum analysis of the following sequence:x = [-1.55, -1.35, -0.33, -0.93, 0.39, 0.45, -0.45, -1.98]In the absence of any other information, this is just a list of numbers. It could be temperature fluctuations in some location from one day to the next, or it could be some normalization of successive samples from a music CD. There is no way to know if the numbers are ``random'' or just ``complicated''.

^{7.2}More than a century ago, before the dawn of quantum mechanics in physics, it was thought that there was no such thing as true randomness--given the positions and momenta of all particles, the future could be predicted exactly; now, ``probability'' is a fundamental component of all elementary particle interactions in the Standard Model of physics [59].

It so happens that, in the example above, the numbers were generated by the

`randn`function in matlab, thereby simulating normally distributed random variables with unit variance. However, this cannot be definitively inferred from a finite list of numbers. The best we can do is estimate the

*likelihood*that these numbers were generated according to some normal distribution. The point here is that any such analysis of noise

*imposes the assumption*that the noise data were generated by some ``random'' process. This turns out to be a very effective model for many kinds of physical processes such as thermal motions or sounds from turbulent flow. However, we should always keep in mind that any analysis we perform is carried out in terms of some underlying

*signal model*which represents

*assumptions*we are making regarding the nature of the data. Ultimately, we are fitting models to data. We will consider only one type of noise: the

*stationary stochastic process*(defined in Appendix C). All such noises can be created by passing white noise through a linear time-invariant (stable) filter [263]. Thus, for purposes of this book, the term

*noise*always means ``filtered white noise''.

#### Testing for White Noise

To test whether a set of samples can be well modeled as white noise, we may compute its*sample autocorrelation*and verify that it approaches an

*impulse*in the limit as the number of samples becomes large; this is another way of saying that successive noise samples are

*uncorrelated*. Equivalently, we may break the set of samples into successive blocks across time, take an FFT of each block, and average their squared magnitudes; if the resulting average magnitude spectrum is

*flat*, then the set of samples looks like white noise. In the following sections, we will describe these steps in further detail, culminating in

*Welch's method*for noise spectrum analysis, summarized in §6.9.

**Next Section:**

Matlab for the Periodogram

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Why Analyze Noise?