Digital Signal Processing
Moslem Amiri, Václav Přenosil
Masaryk University
Understanding Digital Signal Processing, Third Edition, Richard Lyons
(0-13-261480-4) © Pearson Education, 2011.
The Discrete Fourier Transform (2)
2
DFT Resolution, Zero Padding, Frequency-Domain Sampling
 Zero padding
 A method to improve DFT spectral estimation
 Involves addition of zero-valued data samples to
an original DFT input sequence to increase total
number of input data samples
 Investigating zero-padding technique illustrates
DFT’s property of frequency-domain sampling
 When we sample a continuous time-domain function,
having a CFT, and take DFT of those samples, the
DFT results in a frequency-domain sampled
approximation of the CFT
 The more points in DFT, the better DFT output
approximates CFT
3
DFT Resolution, Zero Padding, Frequency-Domain Sampling
4
DFT Resolution, Zero Padding, Frequency-Domain Sampling
 Fig. 3-20
 Because CFT is taken over an infinitely wide time
interval, CFT has continuous resolution
 Suppose we want to use a 16-point DFT to
approximate CFT of f(t) in Fig. 3-20(a)
 16 discrete samples of f(t) are shown on left side of
Fig. 3-21(a)
 Applying those time samples to a 16-point DFT results
in discrete frequency-domain samples, the positive
frequencies of which are represented on right side of
Fig. 3-21(a)
 DFT output comprises samples of Fig. 3-20(b)’s CFT
5
DFT Resolution, Zero Padding, Frequency-Domain Sampling
6
DFT Resolution, Zero Padding, Frequency-Domain Sampling
 Fig. 3-21
 If we append 16 zeros to input sequence and
take a 32-point DFT, we get output shown on
right side of (b)
 DFT frequency sampling is increased by a factor of two
 Adding 32 more zeros and taking a 64-point DFT,
we get output shown on right side of (c)
 64-point DFT output shows true shape of CFT
 Adding 64 more zeros and taking a 128-point
DFT, we get output shown on right side of (d)
 DFT frequency-domain sampling characteristic is
obvious now
7
DFT Resolution, Zero Padding, Frequency-Domain Sampling
 Fig. 3-21
 Although zero-padded DFT output bin index of
main lobe changes as N increases, zero-padded
DFT output frequency associated with main lobe
remains the same
 If we perform zero padding on L nonzero input
samples to get a total of N time samples for an Npoint
DFT, zero-padded DFT output bin center
frequencies are related to original fs by
N
fm
m s
binththeoffrequencycenter
8
DFT Resolution, Zero Padding, Frequency-Domain Sampling
Fig. no.
Main lobe peak
located at m = L = N =
Frequency of main
lobe peak relative to fs
3-21(a) 3 16 16 3fs / 16
3-21(b) 6 16 32 6fs / 32 = 3fs / 16
3-21(c) 12 16 64 12fs / 64 = 3fs / 16
3-21(d) 24 16 128 24fs / 128 = 3fs / 16
9
DFT Resolution, Zero Padding, Frequency-Domain Sampling
 Zero padding
 DFT magnitude expressions
don’t apply if zero padding is used
 To perform zero padding on L nonzero samples of a
sinusoid whose frequency is located at a bin center to get
a total of N input samples, replace N with L above
 To perform both zero padding and windowing on
input, do not apply window to entire input
including appended zero-valued samples
 Window function must be applied only to original nonzero
time samples; otherwise padded zeros will zero out and
distort part of window function, leading to erroneous
results
NAMNAM ocomplexoreal  and2/
10
DFT Resolution, Zero Padding, Frequency-Domain Sampling
 Discrete-time Fourier transform (DTFT)
 DTFT is continuous Fourier transform of an Lpoint
discrete time-domain sequence
 On a computer we can’t perform DTFT because it
has an infinitely fine frequency resolution
 But we can approximate DTFT by performing an Npoint
DFT on an L-point discrete time sequence where
N > L
 Done by zero-padding original time sequence and
taking DFT
11
DFT Resolution, Zero Padding, Frequency-Domain Sampling
 Zero padding
 Zero padding does not improve our ability to
resolve, to distinguish between, two closely
spaced signals in frequency domain
 E.g., main lobes of various spectra in Fig. 3-21 do not
change in width, if measured in Hz, with increased
zero padding
 To improve our true spectral resolution of two
signals, we need more nonzero time samples
 To realize Fres Hz spectral resolution, we must
collect 1/Fres seconds, worth of nonzero time
samples for our DFT processing
12
DFT Processing Gain
 Two types of processing gain associated with
DFTs
 1) DFT’s processing gain
 Using DFT to detect signal energy embedded in noise
 DFT can pull signals out of background noise
 This is due to inherent correlation gain that takes place
in any N-point DFT
 2) integration gain
 Possible when multiple DFT outputs are averaged
13
DFT Processing Gain
 Processing gain of a single DFT
 A DFT output bin can be treated as a bandpass filter
(band center = mfs/N) whose gain can be increased and
whose bandwidth can be reduced by increasing the
value of N
 Maximum possible DFT output magnitude increases as
number of points (N) increases
 Also, as N increases, DFT output bin main lobes
become narrower
 Decreasing a bandpass filter’s bandwidth is useful in
energy detection because frequency resolution
improves in addition to filter’s ability to minimize amount
of background noise that resides within its passband
NAMNAM ocomplexoreal  and2/
14
DFT Processing Gain
DFT of a spectral tone
(a constant-frequency
sinewave) added to
random noise.
Output power levels
are normalized so that
the highest bin output
power is set to 0 dB
first 32 outputs of a
64-point DFT when
input tone is at center
of DFT’s m = 20th bin
because tone’s
original signal power is
below average noise
power level, it is
difficult to detect when
N = 64
if we quadruple the
number of input
samples (N = 256), the
tone power is raised
above average
background noise
power as shown for m
= 80
15
DFT Processing Gain
 Signal-to-noise ratio (SNR)
 DFT’s output signal-power level over the average
output noise-power level
 DFT’s output SNR increases as N gets larger
because a DFT bin’s output noise standard
deviation (rms) value is proportional to , and
DFT’s output magnitude for the bin containing
signal tone is proportional to N
 For real inputs, if N > N′, an N-point DFT’s output
SNRN increases over N′-point DFT SNRN′ by:
 If we increase a DFT’s size from N′ to N = 2N′, DFT’s
output SNR increases by 3 dB
N
)'(log10 10' NNSNRSNR NN 
16
DFT Processing Gain
17
DFT Processing Gain
 Integration gain due to averaging multiple
DFTs
 Theoretically, we could get very large DFT
processing gains by increasing DFT size
 Problem is that the number of necessary DFT
multiplications increases proportionally to N2
 Larger DFTs become very computationally intensive
 Because addition is easier and faster to perform
than multiplication, we can average outputs of
multiple DFTs to obtain further processing gain
and signal detection sensitivity
18
The DFT of Rectangular Functions
 DFT of a rectangular function
 One of the most prevalent and important
computations encountered in DSP
 Seen in sampling theory, window functions,
discussions of convolution, spectral analysis, and
in design of digital filters
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19
The DFT of Rectangular Functions
 DFT of a general rectangular function
 A general rectangular function x(n) is defined as
N samples containing K unity-valued samples
20
The DFT of Rectangular Functions
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21
The DFT of Rectangular Functions
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22
The DFT of Rectangular Functions
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23
The DFT of Rectangular Functions
24
The DFT of Rectangular Functions
 Dirichlet kernel (DFT of rectangular function)
 Has a main lobe, centered about m = 0 point
 Peak amplitude of main lobe is K
 X(0) = sum of K unity-valued samples = K
 Main lobe’s width = 2N/K
 Thus main lobe width is inversely proportional to K
 A fundamental characteristic of Fourier transforms: the
narrower the function in one domain, the wider its
transform will be in the other domain
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25
The DFT of Rectangular Functions
64-point DFT of 64-sample
rectangular function, with 11
unity values (N = 64 and K = 11)
it’s easier to
comprehend the
true spectral
nature of X(m)
by viewing its
absolute
magnitude,
provided in Fig.
3-27(a)
26
The DFT of Rectangular Functions
the main and sidelobes
are clearly evident now
K = 11  peak value of
main lobe = 11
width of main lobe =
N/K = 64/11 = 5.82
27
The DFT of Rectangular Functions
 DFT of a symmetrical rectangular function
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28
The DFT of Rectangular Functions
 DFT of a symmetrical rectangular function
 This DFT is itself a real function
 So it contains no imaginary part or phase term
 If x(n) is real and even, x(n) = x(−n), then Xreal(m) is
nonzero and Ximag(m) is always zero
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29
The DFT of Rectangular Functions
30
The DFT of Rectangular Functions
 Fig. 3-29 (64-point DFT)
 Xreal(m) is nonzero and Ximag(m) is zero
 Identical magnitudes in Figs. 3-27(a) and 3-29(d)
 Shifting K unity-valued samples to center merely
affects phase angle of X(m), not its magnitude (shifting
theorem of DFT)
 Even though Ximag(m) is zero in (c), phase angle
of X(m) is not always zero
 X(m)’s phase angles in (e) are either +π, zero, or −π
 ejπ = ej(−π) = −1 we could easily reconstruct Xreal(m)
from |X(m)| and phase angle Xø(m) if we must
 Xreal(m) is equal to |X(m)| with the signs of |X(m)|’s
alternate sidelobes reversed
31
The DFT of Rectangular Functions
another example of how DFT of a rectangular
function is a sampled version of Dirichlet kernel
a 64-point x(n) where 31
unity-valued samples are
centered about n = 0
index location
by broadening x(n), i.e.,
increasing K, we’ve narrowed
Dirichlet kernel of X(m)
31
64
crossingzerofirst 
K
N
m
peak value of
|X(m)| = K = 31
32
The DFT of Rectangular Functions
 DFT of an all-ones rectangular function
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33
The DFT of Rectangular Functions
34
The DFT of Rectangular Functions
 Fig. 3-32
 Dirichlet kernel of X(m) in (b) is as narrow as it
can get
 Main lobe’s first positive zero crossing occurs at
m = 64/64 = 1 sample in (b)
 Peak value of |X(m)| = N = 64
 x(n) is all ones  |X(m)| is zero for all m ≠ 0
 The sinc function
 Defines overall DFT frequency response to an input
sinusoidal sequence
 Is also amplitude response of a single DFT bin
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35
The DFT of Rectangular Functions
 DFT of an all-ones rectangular function
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36
The DFT of Rectangular Functions
37
The DFT of Rectangular Functions
DFT frequency
axis
representation
Frequency
variable
Resolution of
X(m)
Repetition
interval of
X(m)
Frequency
axis range
Frequency in Hz f fs/N fs -fs/2 to fs/2
Frequency in
cycles/sample
f/fs 1/N 1 -1/2 to 1/2
Frequency in
radians/sample
ω 2π/N 2π -π to π
38
The DFT of Rectangular Functions
 Alternate form of DFT of an all-ones
rectangular function
 Using radians/sample frequency notation for DFT
axis leads to another prevalent form of DFT of allones
rectangular function
 Letting normalized discrete frequency axis
variable be ω = 2πm/N, then πm = Nω/2
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39
Interpreting DFT Using DTFT
40
Interpreting DFT Using DTFT
 Fig. 3-35
 (a) shows an infinite-length continuous-time
signal containing a single finite-width pulse
 Magnitude of its continuous Fourier transform (CFT) is
continuous frequency-domain function X1(ω)
 continuous frequency variable ω is radians per second
 If CFT is performed on infinite-length signal of
periodic pulses in (b), result is line spectra known
as Fourier series X2(ω)
 X2(ω) Fourier series is a sampled version of
continuous spectrum in (a)
41
Interpreting DFT Using DTFT
 Fig. 3-35
 (c) shows infinite-length discrete time sequence
x(n), containing several nonzero samples
 We can perform a CFT of x(n) describing its spectrum
as a continuous frequency-domain function X3(ω)
 This continuous spectrum is called a discrete-time
Fourier transform (DTFT) defined by
 ω frequency variable is measured in radians/sample
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42
Interpreting DFT Using DTFT
 DTFT example
 Time sequence: xo(n) = (0.75)n for n ≥ 0
 Its DTFT is
 Xo(ω) is continuous and periodic with a period of
2π, whose magnitude is shown in Fig. 3-36
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43
Interpreting DFT Using DTFT
 Verification of 2π periodicity of DTFT
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44
Interpreting DFT Using DTFT
45
Interpreting DFT Using DTFT
 Fig. 3-35 (cont.)
 Transforms indicated in Figs. (a) through (c) are
pencil-and-paper mathematics of calculus
 In a computer, using only finite-length discrete
sequences, we can only approximate CFT (the
DTFT) of infinite-length x(n) time sequence in (c)
 That approximation is DFT, and it’s the only Fourier
transform tool available
 Taking DFT of x1(n), where x1(n) is a finite-length
portion of x(n), we obtain discrete periodic X1(m) in (d)
 X1(m) is a sampled version of X3(ω)
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46
Interpreting DFT Using DTFT
 Fig. 3-35
 X1(m) is also exactly equal to CFT of periodic
time sequence x2(n) in (d)
 The DFT is equal to the continuous Fourier
transform (the DTFT) of a periodic time-domain
discrete sequence
 If a function is periodic, its forward/inverse DTFT
will be discrete; if a function is discrete, its
forward/inverse DTFT will be periodic