The DFT is defined only for frequencies
. If we
are analyzing one or more periods of an exactly periodic signal, where the
period is exactly
samples (or some integer divisor of
), then these
really are the only frequencies present in the signal, and the spectrum is
actually zero everywhere but at
,
.
However, we use the
DFT to analyze arbitrary signals from nature. What happens when a
frequency
is present in a signal
that is not one of the
DFT-sinusoid frequencies
?
To find out, let's project a length
segment of a sinusoid at an
arbitrary frequency
onto the
th DFT sinusoid:
The coefficient of projection is proportional to
using the closed-form expression for a geometric series sum once
again. As shown in §6.3-§6.4 above,
the sum is
if
and zero at
, for
. However,
the sum is nonzero at all other frequencies
.
Since we are only looking at
samples, any sinusoidal segment can be
projected onto the
DFT sinusoids and be reconstructed exactly by a
linear combination of them. Another way to say this is that the DFT
sinusoids form a basis for
, so that any length
signal
whatsoever can be expressed as a linear combination of them. Therefore, when
analyzing segments of recorded signals, we must interpret what we see
accordingly.
The typical way to think about this in practice is to consider the DFT
operation as a digital filter for each
, whose input is
and whose output is
at time
.6.4 The
frequency response of this filter is what we just
computed,6.5 and its magnitude is
(shown in Fig.6.3a for
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We see that
is sensitive to all frequencies between dc
and the sampling rate except the other DFT-sinusoid frequencies
for
. This is sometimes called spectral leakage
or cross-talk in the spectrum analysis. Again, there is no
leakage when the signal being analyzed is truly periodic and we can choose
to be exactly a period, or some multiple of a period. Normally,
however, this cannot be easily arranged, and spectral leakage can
be a problem.
Note that peak spectral leakage is not reduced by increasing
.6.7 It can be thought of as being caused by abruptly
truncating a sinusoid at the beginning and/or end of the
-sample time window. Only the DFT sinusoids are not cut off at the
window boundaries. All other frequencies will suffer some truncation
distortion, and the spectral content of the abrupt cut-off or turn-on
transient can be viewed as the source of the sidelobes. Remember
that, as far as the DFT is concerned, the input signal
is the
same as its
periodic extension (more about this in
§7.1.2). If we repeat
samples of a sinusoid at frequency
(for any
), there will be a ``glitch''
every
samples since the signal is not periodic in
samples.
This glitch can be considered a source of new energy over the entire
spectrum. See
Fig.8.3 for an example waveform.
To reduce spectral leakage (cross-talk from far-away
frequencies), we typically use a
window
function, such as a
``raised cosine'' window, to taper the data record gracefully
to zero at both endpoints of the window. As a result of the smooth
tapering, the main lobe widens and the sidelobes
decrease in the DFT response. Using no window is better viewed as
using a rectangular window of length
, unless the signal is
exactly periodic in
samples. These topics are considered further
in Chapter 8.