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 (1)
2
The Discrete Fourier Transform
 Discrete Fourier transform (DFT)
 DFT is a mathematical procedure used to
determine harmonic, or frequency, content of a
discrete signal sequence
 DFT’s origin is continuous Fourier transform X(f)
where x(t) is some continuous time-domain signal
 DFT equation (exponential form)
x(n) is sequence of time-domain sampled values
dtetxfX ftj 2
)()( 



Nmnj
N
n
enxmX /2
1
0
)()( 



3
Understanding the DFT Equation
 DFT equation (rectangular form)
 From Euler’s relationship, e−jø = cos(ø) − jsin(ø)
 m = index of DFT output in frequency domain
m = 0, 1, 2, 3, . . ., N−1
 n = time-domain index of input samples
n = 0, 1, 2, 3, . . ., N−1
 N = number of samples of input sequence and
number of frequency points in DFT output
)]/2sin()/2[cos()(
)()(
1
0
/2
1
0
NmnjNmnnx
enxmX
N
n
Nmnj
N
n











4
Understanding the DFT Equation
 X(m) DFT output
 Each X(m) DFT output term is sum of point-forpoint
product between an input sequence of
signal values and a complex sinusoid of the form
cos(ø) − jsin(ø)
 Exact frequencies of different sinusoids depend
on both sampling rate fs at which the original
signal was sampled, and number of samples N
 The N separate DFT analysis frequencies are
N
mf
mf s
analysis )(
5
Understanding the DFT Equation
6
Understanding the DFT Equation
 Magnitude and power contained in each X(m)
term
222
1
22
)()()()(:spectrumpower
)(
)(
tan)(
)()()()(
)(ofangleanat)()()()(
mXmXmXmX
mX
mX
mX
mXmXmXmX
mXmXmjXmXmX
imagrealmagPS
real
imag
imagrealmag
magimagreal















7
Understanding the DFT Equation
 Example
 We want to sample and perform an 8-point DFT
on a continuous input signal containing
components at 1 kHz and 2 kHz, expressed as
 The 2 kHz term is shifted in phase by 3π/4
radians relative to the 1 kHz sinewave
)4/320002sin(5.0)10002sin()(   tttxin
8
Understanding the DFT Equation
 Example (cont.)
 N = 8  we need 8 input sample values on which
to perform DFT
 Sample rate = fs  sampling every 1/fs = ts sec.
 If we sample xin(t) at fs = 8000 samples/second,
DFT results will indicate what signal amplitude
exists in x(n) at analysis frequencies of mfs/N, or
0 kHz, 1 kHz, 2 kHz, . . ., 7 kHz
 We use DFT equation for m = 0, …, 7
)4/320002sin(5.0)10002sin()()(   sssin ntntntxnx
)]/2sin()/2[cos()()(
1
0
NmnjNmnnxmX
N
n
  


9
Understanding the DFT Equation
10
Understanding the DFT Equation
11
Understanding the DFT Equation
 Example (cont.)







9040.40.0)7(:kHz7
8
kHz87
452414.1414.1)6(:kHz6
8
kHz86
000.00.0)5(:kHz5
8
kHz85
000.00.0)4(:kHz4
8
kHz84
000.00.0)3(:kHz3
8
kHz83
452414.1414.1)2(:kHz2
8
kHz82
9040.40.0)1(:kHz1
8
kHz81





















jmX
N
mf
jmX
N
mf
jmX
N
mf
jmX
N
mf
jmX
N
mf
jmX
N
mf
jmX
N
mf
s
s
s
s
s
s
s
12
Understanding the DFT Equation
 Example (cont.)
 When m = 0
 X(0) is sum of x(n) samples
 X(0) is proportional to average of x(n)
 = N times x(n)’s average value
 X(0) frequency term is the non-time-varying (DC)
component of x(n)
 Our x(n) has no DC component






1
0
1
0
)()]0sin()0[cos()()0(
N
n
N
n
nxjnxX

000.00.0)0(:kHz0
8
kHz80


 jmX
N
mfs
13
Understanding the DFT Equation
14
Understanding the DFT Equation
 Fig. 3-4
 Indicates that xin(t) has signal components at 1
kHz (m = 1) and 2 kHz (m = 2)
 1 kHz tone has a magnitude twice that of 2 kHz
tone
 DFT phase at frequency mfs/N is relative to a
cosine wave at that same frequency of mfs/N Hz
where m = 1, 2, 3, ..., N−1
 E.g., phase of X(1) is −90 degrees, so input sinusoid
whose frequency is 1 · fs/N = 1000 Hz was a cosine
wave having an initial phase shift of −90 degrees (or a
sinewave having an initial phase of zero)
15
Understanding the DFT Equation
 Fig. 3-4
 When DFT input signals are real-valued, DFT
phase at 0 Hz (m = 0, DC) is always zero
because X(0) is always real-only




1
0
)()0(
N
n
nxX
16
DFT Symmetry
 Fig. 3-4
 There is a symmetry in DFT results
 When input sequence x(n) is real, complex DFT
outputs for m = 1 to m = (N/2) − 1 are redundant
with frequency output values for m > (N/2)
for m = 1, 2, 3, . . . , N−1
 To obtain DFT of x(n), we need only compute the first
N/2+1 values of X(m) where 0 ≤ m ≤ (N/2); X(N/2+1) to
X(N−1) DFT output terms provide no additional
information about spectrum of real sequence x(n)
)()(
degrees)(at)(
degrees)(at)()(
mNXmX
mNXmNX
mXmXmX






17
DFT Symmetry
 Proving symmetry of DFT of real input
sequences
 X(N−m) is merely X(m) with the sign reversed on
X(m)’s exponent
















 

 


1
0
/21)2sin()2cos(
/22
1
0
/)(2
1
0
onsubstituti
/2
1
0
)(
)(
)()(
)()(
2
N
n
Nmnjnjne
Nmnjnj
N
n
NmNnj
N
n
Nmnj
N
n
enx
eenx
enxmNX
enxmX
nj





18
DFT Symmetry
 An additional symmetry property of DFT
 Determine DFT of real input functions where
input index n is defined over both positive and
negative values
 If real input function is even, x(n) = x(−n), then
X(m) is always real and even
 Xreal(m) is in general nonzero and Ximag(m) is zero
 If real input function is odd, x(n) = −x(−n), then
Xreal(m) is always zero and Ximag(m) is, in general,
nonzero
19
DFT Linearity
 DFT has linearity property
 Proof
)()()()()()(
)()(
)()(
2121
22
11
mXmXmXnxnxnx
mXnx
mXnx
sum
DFT
sum
DFT
DFT
 
 
 
 
)()(
)()(
)()()(
)()(
21
/2
1
0
2
/2
1
0
1
/2
1
0
21
/2
1
0
mXmX
enxenx
enxnxmX
enxmX
Nmnj
N
n
Nmnj
N
n
Nmnj
N
n
sum
Nmnj
N
n






















20
DFT Magnitudes
 Output magnitude of DFT
 When a real input signal contains a sinewave
component of peak amplitude Ao with an integral
number of cycles over N input samples, output
magnitude of DFT for that sinewave is
 For real inputs, hardware memory registers must be
able to hold values as large as N/2 times the maximum
amplitude of input sample values
 If DFT input is a complex sinusoid of magnitude
Ao (i.e., Aoej2πfnts) with an integer number of cycles
over N samples, output magnitude of DFT for that
sinewave is
2/NAM or 
NAM oc 
21
DFT Magnitudes
 DFT is occasionally defined as
 Some commercial software packages use
 Forward and inverse DFTs
 Scale factors are used so that there’s no scale
change when transforming in either direction
Nmnj
N
n
enx
N
mX /2
1
0
)(
1
)(' 



Nmnj
N
m
Nmnj
N
n
emX
N
nx
enx
N
mX
/2
1
0
/2
1
0
)(''
1
)(
)(
1
)(''











22
Summary
 To recap what we’ve learned so far
 Each DFT output term is sum of term-by-term
products of an input time-domain sequence with
a sine and a cosine wave sequences
 For real inputs, an N-point DFT’s output provides
only N/2+1 independent terms
 DFT is a linear operation
 Magnitude of DFT results is directly proportional
to N
 DFT’s frequency resolution (spacing) is fs/N
 X(m = N/2) corresponds to mfs/N = fs/2 = folding
(Nyquist) frequency
23
DFT Shifting Theorem
 Shifting theorem property of DFT
 A shift in time of a periodic x(n) input sequence
manifests itself as a constant phase shift in
angles associated with DFT results
 If we sample x(n) starting at n = k (integer), DFT
of those time-shifted sample values is Xshifted(m)
where
 If the point where we start sampling x(n) is shifted
to right by k samples, DFT output spectrum of
Xshifted(m) is X(m) with each of X(m)’s complex
terms multiplied by linear phase shift ej2πkm/N,
which is merely a phase shift of 2πkm/N radians
)()( /2
mXemX Nmkj
shifted


24
DFT Shifting Theorem
 Example
 Suppose we sampled preceding DFT example
input sequence later in time by k = 3 samples
xin(t) = sin(2π1000t) + 0.5sin(2π2000t+3π/4)
25
DFT Shifting Theorem
26
DFT Shifting Theorem
27
DFT Shifting Theorem
 Fig. 3-6
 Magnitude of Xshifted(m) should be unchanged
from that of X(m)
 Fig. 3-6(a) is identical to Fig. 3-4(a)
 Phase of DFT result does change depending on
the instant at which we started to sample xin(t)
 E.g., X(1) from preceding DFT Example had a
magnitude of 4 at a phase angle of −π/2
 k = 3 and N = 8
 Xshifted(1): magnitude of 4, phase angle of π/4
4/)8/48/6(
2/8/132/2
44
4)1()1(


jj
jjNmkj
shifted
ee
eeXeX




28
Inverse DFT
 Inverse discrete Fourier transform (IDFT)
 We can obtain the original time-domain signal by
performing IDFT on X(m) frequency-domain
values
 A discrete time-domain signal can be considered the
sum of various sinusoidal analytical frequencies and
that the X(m) outputs of the DFT are a set of N
complex values indicating the magnitude and phase
of each analysis frequency comprising that sum
)]/2sin()/2[cos()(
1
)(
1
)(
1
0
/2
1
0
NnmjNnmmX
N
emX
N
nx
N
m
Nnmj
N
m










29
DFT Leakage
 Leakage
 Causes DFT results to be only an approximation
of the true spectra of the original input signals
prior to digital sampling
 Although there are ways to minimize leakage, we
can’t eliminate it entirely
30
DFT Leakage
 Leakage
 DFT produces correct results only when input
data sequence contains energy precisely at
integral multiples of fundamental frequency fs/N
 If input has a signal component at some
intermediate frequency between analytical
frequencies of mfs/N, say 1.5fs/N, this input signal
will show up to some degree in all of N output
analysis frequencies of DFT
 We say that input signal energy shows up in all of
DFT’s output bins
 DFT samples = “bins”
1,...,2,1,0where,)(  Nm
N
mf
mf s
analysis
31
DFT Leakage
32
DFT Leakage
an input sequence having 3.4
cycles over N = 64 samples
because input sequence
does not have an integral
number of cycles over
64-sample interval, input
energy has leaked into all
the other DFT output bins
m = 4 bin, for example, is
not zero because sum of
products of input sequence
and m = 4 analysis
frequency is no longer zero
33
DFT Leakage
 Cause of leakage
 For a real cosine input having k cycles (k need
not be an integer) in N-point input time sequence,
amplitude response of an N-point DFT bin in
terms of bin index m is approximated by sinc
function
 Ao is peak value of DFT’s input sinusoid
 For our examples here, Ao is unity
)(
)](sin[
2
)(
mk
mkNA
mX o





34
DFT Leakage
the curve comprises a
main lobe and periodic
peaks and valleys
known as sidelobes
DFT output will be a
sampled version of the
continuous spectrum
when DFT’s input
sequence has exactly
an integral k number of
cycles (centered
exactly in the m = k
bin), no leakage occurs
35
DFT Leakage
 Example (Fig. 3-10(a))
 A real 8 kHz sinusoid, having unity amplitude, is
sampled at a rate of fs = 32000 samples/second
 If we take a 32-point DFT of samples, DFT’s
frequency resolution, or bin spacing, is fs/N =
32000/32 Hz = 1.0 kHz
 We can predict DFT’s magnitude response by
centering input sinusoid’s spectral curve at positive
frequency of 8 kHz
 DFT output is a sampled version of continuous
spectral curve
 DFT outputs reside on continuous spectrum at its
peak and exactly at curve’s zero crossing points
36
DFT Leakage
frequency-domain
sampling results
in nonzero
magnitudes for all
DFT output bins
results in the
leaky DFT output
shown
37
DFT Leakage
If the continuous spectra that
we’re sampling are
symmetrical, why does DFT
output in this Figure look so
asymmetrical?
Bins to the right of third bin
are decreasing in amplitude
faster than bins to the left of
third bin
With k = 3.4 and m = 3, from
sinc function the X(3) bin’s
magnitude should be 24.2—
but Fig. (b) shows X(3) bin
magnitude to be greater than
25. Why?
38
DFT Leakage
39
DFT Leakage
 Fig. 3-11
 When examining a DFT output, we’re normally
interested only in m = 0 to m = (N/2−1) bins
 Thus, only the first 32 bins are shown in Fig. 3-8(b)
 DFT is periodic in frequency domain
 Upon examining DFT’s output for higher frequencies,
we end up going in circles, and spectrum repeats itself
forever
 Fig. 3-11 shows cyclic representation of 64-point DFT
shown in Fig. 3-8(b)
 The more conventional way to view a DFT output
is to unwrap the spectrum in Fig. 3-11 to get the
spectrum in Fig. 3-12
40
DFT Leakage
41
DFT Leakage
 Fig. 3-12
 As some of the input 3.4-cycle signal amplitude
leaks into 2nd bin, 1st bin, and 0th bin, leakage
continues into −1st bin, −2nd bin, −3rd bin, etc
 63rd bin = −1st bin, 62nd bin = −2nd bin, and so on
 These bin equivalencies allow us to view DFT output
bins as if they extend into negative-frequency range,
as shown in Fig. 3-13(a)
 Result is that the leakage wraps around m = 0, as
well as around m = N
 m = 0 frequency is m = N frequency
 Leakage wraparound at m = 0 accounts for the
asymmetry around DFT’s m = 3 bin in Fig. 3-8(b)
42
DFT Leakage
43
DFT Leakage
 Fig. 3-13(b)
 when a DFT input sequence x(n) is real, DFT
outputs from m = 0 to m = (N/2−1) are redundant
with frequency bin values for m > (N/2)
 |X(m)| = |X(N−m)|
 This means that leakage wraparound also occurs
around m = N/2 bin
 This can be illustrated using an input of 28.6 cycles per
sample interval (32 − 3.4) whose spectrum is shown in
Fig. 3-13(b)
 Figs. 3-13(a) and (b) are similar
44
DFT Leakage
45
DFT Leakage
 Fig. 3-14
 DFT exhibits leakage wraparound about m = 0
and m = N/2 bins
 Minimum leakage asymmetry will occur near
N/4th bin
46
Windows
 Windowing
 Windowing reduces DFT leakage by minimizing
magnitude of sinc function’s sidelobes
 Done by forcing amplitude of input time sequence at
both beginning and end of sample interval to go
smoothly toward a single common amplitude value
47
Windows
48
Windows
 Fig. 3-15
 Considering infinite-duration time signal shown in
(a), a DFT can only be performed over a finitetime
sample interval like that shown in (c)
 We can think of DFT input signal in (c) as product
of (a), and rectangular window whose magnitude
is 1 over sample interval shown in (b)
 Anytime we take DFT of a finite-extent input
sequence, we are, by default, multiplying that
sequence by a window of all ones
 Sinc function’s sin(x)/x shape is caused by this
rectangular window because CFT of rectangular
window in (b) is sinc function
49
Windows
 Fig. 3-15
 Rectangular window’s abrupt changes between
one and zero cause sidelobes in sinc function
 To minimize spectral leakage caused by those
sidelobes, we have to reduce sidelobe amplitudes by
using window functions other than rectangular window
 If we multiply DFT input, (c), by triangular window
function, (d), to obtain windowed input signal, (e),
values of final input signal appear to be the same
at beginning and end of sample interval in (e)
 Reduced discontinuity decreases level of relatively
high-frequency components in overall DFT output; that
is, DFT bin sidelobe levels are reduced in magnitude
using a triangular window
50
Windows
 Fig. 3-15
 There are window functions that reduce leakage
even more than triangular window
 Hanning window in (f)
 Product of window in (f) and input sequence provides
signal shown in (g) as input to DFT
 Hamming window in (h)
 It’s much like Hanning window, but it’s raised on a
pedestal
51
Windows
1,...,2,1,0for
2
cos46.054.0)(:windowHamming
1,...,2,1,0for
2
cos5.05.0)(:windowHanning
1,...,22/,12/for
2/
2
2/,...,2,1,0for
2/)(:windowTriangular
1,...,2,1,0for,1)(:r windowRectangula
)()()(
1
0
/2























 



Nn
N
n
nw
Nn
N
n
nw
NNNn
N
n
Nn
N
n
nw
Nnnw
enxnwmX
N
n
Nmnj
w



52
Windows
53
Windows
 Fig. 3-16
 Hamming, Hanning, and triangular give reduced
sidelobe levels relative to rectangular
 Because Hamming, Hanning, and triangular
reduce time-domain signal levels, their main lobe
peak values are reduced relative to rectangular
 Log magnitude response (normalized)









)0(
)(
log20)( 10
W
mW
mWdB
54
Windows
 Fig. 3-16(b)
 Main lobe of rectangular window’s magnitude
response is the most narrow, fs/N
 However, its first sidelobe level is only −13 dB below
main lobe peak, which is not good
 Various nonrectangular windows’ wide main
lobes degrade the windowed DFT’s frequency
resolution by almost a factor of two
 However, important benefits of leakage reduction
usually outweigh the loss in DFT frequency resolution
 Hanning has rapid sidelobe roll-off
 Hamming has lower first sidelobe levels, but its
sidelobes roll off slowly relative to Hanning
55
Windows
shape of Hanning
window’s response
looks broader and
has a lower peak
amplitude, but its
sidelobe leakage is
noticeably reduced
from that of
rectangular window
56
Windows
57
Windows
 Windows
 Overall frequency resolution and signal sensitivity
are affected much more by size and shape of
window function than mere size of DFTs
 There are many different window functions
described in literature of DSP
 Window selection is a trade-off between main
lobe widening, first sidelobe levels, and how fast
the sidelobes decrease with increased frequency
 Use of any particular window depends on application
58
DFT Scalloping Loss
 Scalloping
 Fluctuations in overall magnitude response of an
N-point DFT
 When no input windowing function is used,
sin(x)/x shape of sinc function’s magnitude
response applies to each DFT output bin
59
DFT Scalloping Loss
60
DFT Scalloping Loss
 Fig. 3-19
 (a) shows a DFT’s aggregate magnitude
response by superimposing several sin(x)/x bin
magnitude responses
 Sinc function’s sidelobes are not key here
 In (b), overall DFT frequency-domain response is
indicated by bold envelope curve
 This rippled curve, also called picket fence effect,
illustrates processing loss for input frequencies
between bin centers
 Magnitude of DFT response fluctuates from 1.0, at bin
center, to 0.637 halfway between bin centers
 This envelope ripple exhibits a scalloping loss of −4 dB
halfway between bin centers
61
DFT Scalloping Loss
 Fig. 3-19
 Illustrates a DFT output when no window (i.e., a
rectangular window) is used
 Because nonrectangular window functions
broaden DFT’s main lobe, their use results in a
scalloping loss that will not be as severe as with
rectangular window
 Their wider main lobes overlap more and fill in valleys
of envelope curve in (b)
 Scalloping loss isn’t a severe problem in practice
 Real-world signals normally have bandwidths that span
many frequency bins so that DFT magnitude response
ripples can go almost unnoticed