The frequency
12
T
s
1
2
T
s
, known today as the Nyquist frequency and
the Shannon sampling frequency, corresponds to the
highest frequency at which a signal can contain energy and
remain compatible with the Sampling Theorem. High-quality
sampling systems ensure that no aliasing occurs by
unceremoniously lowpass filtering the signal (cutoff frequency
being slightly lower than the Nyquist frequency) before
sampling. Such systems therefore vary the
anti-aliasing filter's cutoff frequency as
the sampling rate varies. Because such quality features cost
money, many sound cards do not have
anti-aliasing filters or, for that matter, post-sampling
filters. They sample at high frequencies, 44.1 kHz for example,
and hope the signal contains no frequencies above the Nyquist
frequency (22.05 kHz in our example). If no aliasing occurs, we
can use digital signal processing to reduce the sampling rate
without incurring further aliasing. If, however, the signal
contains frequencies beyond the sound card's Nyquist frequency,
the resulting aliasing can be impossible to remove.
To gain a better appreciation of aliasing,
sketch the spectrum of a sampled square wave. For
simplicity consider only the spectral repetitions centered
at
-1
T
s
-1
T
s
,
00,
1
T
s
1
T
s
. Let the sampling interval
T
s
T
s
be 1; consider two values for the square wave's period: 3.5
and 4. Note in particular where the spectral lines go as the
period decreases; some will move to the left and some to the
right. What property characterizes the ones going the same
direction?
The square wave's spectrum is shown by the bolder set of
lines centered about the origin. The dashed lines correspond
to the frequencies about which the spectral repetitions (due
to sampling with
T
s
=1
T
s
1
) occur. As the square wave's period decreases, the
negative frequency lines move to the left and the positive
frequency ones to the right.
If we satisfy the Sampling Theorem's conditions,
the signal will change only slightly during each pulse. As we
narrow the pulse, making
ΔΔ
smaller and smaller, the nonzero values of the signal
st
p
T
s
t
s
t
p
T
s
t
will simply be
sn
T
s
s
n
T
s
, the signal's samples. If indeed the
Nyquist frequency equals the signal's highest frequency, at
least two samples will occur within the period of the signal's
highest frequency sinusoid. In these ways, the sampling signal
captures the sampled signal's temporal variations in a way that
leaves all the original signal's structure intact.
What is the simplest bandlimited signal?
Using this signal, convince yourself that less than two
samples/period will not suffice to specify it. If the
sampling rate
1
T
s
1
T
s
is not high enough, what signal would your resulting
undersampled signal become?
The simplest bandlimited signal is the sine wave. At the
Nyquist frequency, exactly two samples/period would
occur. Reducing the sampling rate would result in fewer
samples/period, and these samples would appear to have
arisen from a lower frequency sinusoid.