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Downsampling and Aliasing

The downsampling operator $ \hbox{\sc Downsample}_M$ selects every $ M^{th}$ sample of a signal:

$\displaystyle \zbox {\hbox{\sc Downsample}_{M,n}(x) \isdef x(Mn)}
$

The aliasing theorem states that downsampling in time corresponds to aliasing in the frequency domain:

$\displaystyle \zbox {\hbox{\sc Downsample}_M(x) \leftrightarrow \frac{1}{M} \hbox{\sc Alias}_M(X)}
$

where the $ \hbox{\sc Alias}$ operator is defined as

$\displaystyle \zbox {\hbox{\sc Alias}_{M,\omega}(X) \isdef \sum_{k=0}^{M-1} X\left(\omega+k\frac{2\pi}{M}\right)}
$

for $ \omega\in[-\pi,\pi]$. The summation terms for $ k\neq 0$ are called aliasing components.

In z transform notation, the $ \hbox{\sc Alias}$ operator can be expressed as [264]

$\displaystyle \hbox{\sc Alias}_{M,z}(X)
= \sum_{k=0}^{M-1} X\left(W_M^k z^\frac{1}{M}\right)
$

where $ W_M\isdef e^{j2\pi/M}$ is a common notation for the primitive $ M$th root of unity. On the unit circle of the $ z$ plane, this becomes

$\displaystyle \hbox{\sc Alias}_{M,\omega}(X)
=
\sum_{k=0}^{M-1} X\left(e^{j\le...
...frac{\omega}{M} + k\frac{2\pi}{M}\right)}\right),
\quad -\pi\leq \omega < \pi.
$

The frequency scaling corresponds to having a sampling inverval of $ T=1$ after downsampling, which corresponds to the interval $ T=1/M$ prior to downsampling.

The aliasing theorem makes it clear that, in order to downsample by factor $ M$ without aliasing, we must first lowpass-filter the spectrum to $ [-\pi / M, \pi / M]$. This filtering (when ideal) zeroes out the spectral regions which alias upon sampling.

Note that any rational sampling-rate conversion factor $ \rho = L/M$ may be implemented as an upsampling by the factor $ L$ followed by downsampling by the factor $ M$ [46,264]. Conceptually, a stretch-by-$ L$ is followed by a lowpass filter cutting off at $ \omega_c \isdef \pi/(L\;\max\;M)$, followed by downsample-by-$ L$, i.e.,

$\displaystyle x^\prime = \hbox{\sc Downsample}_M\{\hbox{\sc Lowpass}_{\omega_c}[\hbox{\sc Stretch}_L(x)]\}
$

In practice, there are more efficient algorithms for sampling-rate conversion [247] based on a more direct approach to bandlimited interpolation.



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``Spectral Audio Signal Processing'', by Julius O. Smith III, (March 2007 Draft).
Copyright © 2008-05-15 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA),   Stanford University
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