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Autocorrelation

The cross-correlation of a signal with itself gives its autocorrelation:

$\displaystyle \zbox {{\hat r}_x(l) \isdef \frac{1}{N}(x\star x)(l)
\isdef \frac{1}{N}\sum_{n=0}^{N-1}\overline{x(n)} x(n+l)}
$

The autocorrelation function is Hermitian:

$\displaystyle {\hat r}_x(-l) = \overline{{\hat r}_x(l)}
$

When $ x$ is real, its autocorrelation is real and even (symmetric about lag zero).

The unbiased cross-correlation similarly reduces to an unbiased autocorrelation when $ x\equiv y$ :

$\displaystyle \zbox {{\hat r}^u_x(l) \isdef \frac{1}{N-l}\sum_{n=0}^{N-1-l} \overline{x(n)} x(n+l),\quad l = 0,1,2,\ldots,L-1} \protect$ (8.2)

The DFT of the true autocorrelation function $ r_x(n)\in{\bf R}^N$ is the (sampled) power spectral density (PSD), or power spectrum, and may be denoted

$\displaystyle R_x(\omega_k) \isdef \hbox{\sc DFT}_k(r_x).
$

The complete (not sampled) PSD is $ R_x(\omega) \isdef
\hbox{\sc DTFT}_k(r_x)$ , where the DTFT is defined in Appendix B (it's just an infinitely long DFT). The DFT of $ {\hat r}_x$ thus provides a sample-based estimate of the PSD:8.10

$\displaystyle {\hat R}_x(\omega_k)=\hbox{\sc DFT}_k({\hat r}_x) = \frac{\left\vert X(\omega_k)\right\vert^2}{N}
$

We could call $ {\hat R}_x(\omega_k)$ a ``sampled sample power spectral density''.

At lag zero, the autocorrelation function reduces to the average power (mean square) which we defined in §5.8:

$\displaystyle {\hat r}_x(0) \isdef \frac{1}{N}\sum_{m=0}^{N-1}\left\vert x(m)\right\vert^2 % \isdef \Pscr_x^2
$

Replacing ``correlation'' with ``covariance'' in the above definitions gives corresponding zero-mean versions. For example, we may define the sample circular cross-covariance as

$\displaystyle \zbox {{\hat c}_{xy}(n)
\isdef \frac{1}{N}\sum_{m=0}^{N-1}\overline{[x(m)-\mu_x]} [y(m+n)-\mu_y].}
$

where $ \mu_x$ and $ \mu_y$ denote the means of $ x$ and $ y$ , respectively. We also have that $ {\hat c}_x(0)$ equals the sample variance of the signal $ x$ :

$\displaystyle {\hat c}_x(0) \isdef \frac{1}{N}\sum_{m=0}^{N-1}\left\vert x(m)-\mu_x\right\vert^2 \isdef {\hat \sigma}_x^2
$


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``Mathematics of the Discrete Fourier Transform (DFT), with Audio Applications --- Second Edition'', by Julius O. Smith III, W3K Publishing, 2007, ISBN 978-0-9745607-4-8.
Copyright © 2014-04-21 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA),   Stanford University
CCRMA