EE 324
Digital Signal Processing
Chapter 01
INTRODUCTION
Prof. Tarek I Haweel
Professor,
Department of Electrical Engineering, Majmaah University
Email: t.haweel@mu.edu.sa
Signals and Signal Processing
• Signals play an important role in our daily
life.
• A signal is a function of independent
variables such as
–
–
–
–
–
TIME,
DISTANCE,
POSITION,
TEMPERATURE, and
PRESSURE.
• Some Examples of Signals are shown next.
1
Examples of Typical Signals (1/10)
• Speech and Music Signals
– Represent AIR PRESSURE as function of TIME at a
point in space.
• Waveform of a speech signal “I like Digital
Signal Processing” is shown below:
2
Examples of Typical Signals (2/10)
• Electrocardiography (ECG) Signal
– Represent the ELECTRICAL ACTIVITY of the heart.
• A typical ECG signal is shown below:
3
Examples of Typical Signals (3/10)
• The ECG trace is a periodic waveform
• One period of the waveform shown below
represents
4
Examples of Typical Signals (4/10)
• Electroencephalogram (EEG) Signals:
– Represent the electrical activity caused by the
random firing of billions of neurons in the brain
5
Examples of Typical Signals (5/10)
• Seismic Signals:
– Caused by the movements of ROCKS resulting
from an earthquake, o volcanic eruption or an
underground explosion.
• The ground movement generates three types
of elastic waves that propagate through the
body of earth in all directions from the source
of movement.
6
Examples of Typical Signals (6/10)
• Typical Seismograph record:
– Vertical Ground Velocity
7
Examples of Typical Signals (7/10)
• Typical Seismograph record:
– North Ground Velocity
8
Examples of Typical Signals (8/10)
• Typical Seismograph record:
– East Ground Velocity
9
Examples of Typical Signals (9/10)
• Black and White Picture
– Represents light intensity as function of two
spatial coordinates
10
Examples of Typical Signals (10/10)
• Video Signal
– Consists of a sequence of images, called frames,
and is function of three variables: Two spatial
coordinates and TIME
11
Signals and Signal Processing
12
• Most signals we encounter, are generated
naturally.
– However, a signal can also be generated
synthetically or by computer
• A signal carries information.
• Objective of signal processing:
– Extract the useful information carried by the signal
• Method of information extraction:
– Depends on the type of the signal and the nature of
the information carried by the signal
• This course is concerned with the discretetime representation of signals and their
discrete-time processing
Signal Classification (1/6)
13
• Deterministic Signals
– A deterministic signal can be uniquely described
by an explicit mathematical expression, a table of
data, or a well defined rule.
– Here all past, present and future values of the
signal are known precisely, without any uncertainty
• Random Signals
– The signals whose time evolution can not be
predicted, are said to be random in nature, e.g.,
noise generated by seismic signal, speech signals,
etc.
– Random signals are characterized by their
statistical parameters:
• Mean, standard deviation, variance, etc.
Signal Classification (2/6)
14
• Multichannel Signals
– A signal generated by more than one sources or
sensors
– An ECG signal obtained through 3-leads or 12-leads
would result in a 3-channel or 12-channel signal.
– Multichannel signals are represented in vector
form, e.g., in 3-lead case:
 s1 (t ) 


s (t )  s2 (t )


 s3 (t ) 
Signal Classification (3/6)
15
• Multidimensional Signals
– A signal whose values depends on more than one
independent variables.
– A still Black & White image is a 2-dimentional
signal, as each pixel value depends on two spatial
coordinates; horizontal and vertical position and
can be expressed as I (x, y).
– A Black & White TV picture can be mathematically
written as I (x, y, t). Thus brightness or image
intensity, for a particular spatial coordinates, varies
with time  3-dimensional signal
Signal Classification (4/6)
16
• Color TV Signal:
– The picture can be described by three intensity
functions, corresponding to the strength of three
principle colors: RED, GREEN and BLUE, as
Ir (x, y, t), Ig (x, y, t), Ib (x, y, t)
– Thus color TV picture is an example of 3-channel, 3dimensional signal and can be mathematically
expressed as:
 I r ( x, y , t ) 


I ( x, y , t )   I g ( x, y , t ) 
 I b ( x, y, t ) 
Signal Classification (5/6)
Analog or Continuous Time Signal
Sampling at discrete intervals
Discrete-time or Sampled Signal
Quantize the signals’ values
to a set of discrete values
Digital Signal
17
Signal Classification (6/6)
18
Typical Signal Processing Applications
• Most signal processing operations in case of
analog signals are carried out in the timedomain.
• In case of discrete-time signals, both timedomain or frequency-domain operations are
usually employed.
19
Elementary Time Domain Operations
• Amplitude Scaling
– Amplification
– Attenuation
• Time Shift
– Delayed
– Advance
• Time Scaling
– Stretching
– Compressing
• Addition / Multiplication
• Integration / Differentiation
20
Filtering (1/9)
• Filtering is one of the most widely used
complex signal processing operations
• The system implementing this operation is
called FILTER
• A filter PASSES certain frequency
components without any distortion and
BLOCKS other frequency components
completely
– The range of frequencies that is allowed to PASS
is called the PASSBAND of the filter and
– the range of frequencies that is BLOCKED is
called the STOPBAND of the filter
21
22
Filtering (2/9)
• The filtering operation for linear analog filter
is described by the convolution integral

y (t )   h(t   ) x( )d

• Where x(t) is input signal, y(t) is the output
of the filter, and h(t) is the impulse response
of the filter.
Filtering (3/9)
• Lowpass Filter
– Passes all low-frequency components below a
certain frequency fc, called the cutoff frequency,
and blocks all high-frequency components
above fc.
• Highpass Filter
– Passes all high-frequency components above a
certain cutoff frequency fc, and blocks all highfrequency components below fc.
23
Filtering (4/9)
• Bandpass Filter
– Passes all frequency components between two
cutoff frequencies fc1 and fc2 where fc1< fc2, and
blocks all frequency components below the
frequency fc1 and above the frequency fc2.
• Bandstop Filter
– Blocks all frequency components between two
cutoff frequencies fc1 and fc2 where fc1< fc2, and
passes all frequency components below the
frequency fc1 and above the frequency fc2.
24
Filtering (5/9)
There are various other types of filters:
• A filter blocking a single frequency
components is called NOTCH filter
• A MULTIBAND filter has more than one
stopband and more than one passband
• A COMB filter blocks all frequencies that are
integral multiple of a certain low frequency
25
Filtering (6/9)
• In many applications the desired signal
occupies a low frequency band from dc to fL
Hz, and gets corrupted by a high frequency
NIOSE with frequency components above fH
Hz where fL < fH.
• In such cases the desired signal can be
recovered from the noise-corrupted signal
by passing the later through a lowpass filter
with a cutoff frequency fC, where fL < fC < fH.
26
Filtering (7/9)
• A common source of noise is power lines
radiating electric and magnetic fields
• The noise generated by the power lines
appears a 60 Hz sinusoidal signal
corrupting the desired signal and can be
removed by passing the corrupted signal
through a NOTCH filter with NOTCH
FREQUENCY at 60 Hz.
27
Filtering (8/9)
• Consider an input signal consisting of three
sinusoidal components of frequencies 50
Hz, 110 Hz, and 210 Hz
28
Filtering (9/9)
• Output of different filters:
29
Modulation and Demodulation (1/2)
• For efficient transmission of a lowfrequency signal over a channel, it is
necessary to transform the signal to a highfrequency signal by means of a
MODULATION operation.
• At the receiving end the modulated highfrequency is DEMODULATED to
extract/recover the desired low-frequency
signal.
30
Modulation and Demodulation (2/2)
• There are 4 types of modulation of analog
signals:
• Amplitude Modulation
• Frequency Modulation
• Phase Modulation
• Pulse Amplitude Modulation
31
Why Digital Signal Processing (1/3)
32
• Guaranteed Accuracy
– The accuracy is determined by yhe number of bits
used
• Perfect Reproducibility
– Identical performance from unit to unit is obtained,
since there are no variations due to component
tolerances, e.g., using DSP techniques, a digital
recording can be copied or reproduced several
times without any degradation in the signal
quality.
• No drift in performance with temperature or
age.
Why Digital Signal Processing (2/3)
• Advanced Semiconductor Technology
–
–
–
–
–
Greater reliability
Smaller Size
Low Cost
Low Power Consumption
High Speed
• Greater Flexibility
– DSP systems can be programmed and reprogrammed to perform a variety of functions,
without modifying the hardware  perhaps the
most important feature of DSP.
33
Why Digital Signal Processing (3/3)
• Superior Performance
– DSP can be used to performed functions not
possible with analog signal processing, e.g.
Complex adaptive filtering algorithms can be
implemented with DSP techniques
• In some cases, the information may already
be in digital form and DSP offers the only
viable solution
• DSP is not without disadvantages; e.g.,
– Cost and Speed
– Design Time
– Finite Word Length Effects
But the significance of these disadvantages is being
continuously diminished by the new technology.
34
Digital Processing of Analog Signals
In this course we
are concerned
with discrete-time
signal processing
35
Overview of DSP Applications (1/3)
• Cellular/Mobile Telephony
–
–
–
–
Speech and Channel Coding
Voice and data processing
Power Management
Multipath equalization and Echo Cancellation
• Digital Audio
– Stereo and Surround sound
– Audio equalization and Mixing
– Electronic music
• Automotive
– Digital Audio
– Digital Radio
– Personal Communication System
– Noise Reduction (Engine)
36
Overview of DSP Applications (2/3)
• Personal Computer
–
–
–
–
–
Sound cards
Data Storage and retriveal
Multimedia
Modems
Internet phones, music and video
• Medical Electronics
–
–
–
–
Critical/intensive care monitors
Digital X-rays
Medical Imaging – CT Scans, MRI, Ultrasound
ECG/EEG Recording and analysis
• Digital Telephone
– DTMF generation and detection
– Topless answering machines
– Speech Synthesizer
37
Overview of DSP Applications (3/3)
• Military
– Radar
– Sonar
– Secure Communication
• Industrial
– Oil and mineral processing
– Process monitoring and control
– NDT – Non destructive testing
• Scientific
–
–
–
–
Earthquake recording and analsysi
Data acquisition
Spectral analysis
Simulation and recording
38
DSP and Other Areas
39
• DSP is very interdisciplinary, relying on the
technical work in many adjacent fields. The
borders between DSP and other technical
disciplines are not sharp and well defined, but
rather fuzzy and overlapping.
If you want to
specialize in DSP,
these are the allied
areas you will also
need to study.
Audio Applications of DSP (1/6)
40
1. DIGITAL AUDIO MIXING
– The audio mixing system is a prime example of
where DSP has been successfully employed to
improve the audio quality and enhance its
functionality.
– A digital audio system includes facilities for
• Audio equalization (EQs)
• Audio Mixing &
• Post-mix Processing, as shown below
Audio
Input
Signals
1
1
.
Pre-mix
. Processing
. (EQ’s Control)
32
.
.
.
32
1
.
Mix Matrix .
(32 x 16) .
16
Post-mix 1
Processing
Processed
Audio
Outputs
Post-mix 16
Processing
Audio Applications of DSP (2/6)
1. DIGITAL AUDIO MIXING
– The digital Audio Equalizer (EQ) is a set of digital
filters, with adjustable characteristics, that is used
to manipulate parts of frequency bands of the
audio inputs to achieve the desired sound.
– The equalized audio signals are then mixed by
using a mix matrix facility which allows any and
every audio input to be mixed to every output
– The post-mix processing includes adding artificial
echo's and reverberation to simulate actual
listening environments, e.g., concert halls
41
Audio Applications of DSP (3/6)
2. SPEECH SYNTHESIS
– The advances in semiconductor technology and
DSP have made it possible to obtain a speech
quality that is indistinguishable from human
speech.
– SPEAK-n-SPELL: It is an example of a successful
commercial product with speech output. It is an
electronic learning aid for children and uses, LPC
(Linear Predictive Coding) technique.
– In LPC, the actual human speech, to be produced
later, is modeled as the response of a time varying
digital filter to a periodic or a random excitation
signal.
42
43
Audio Applications of DSP (4/6)
2. SPEECH SYNTHESIS
– The periodic excitation is used for voiced sounds
(e.g. vowels) and represents the air flow through
the vocal cords as they vibrate.
– The random excitation is used for unvoiced
sounds (e.g. s, sh), and represents the noise
created by forcing air past constrictions in the
vocal tract.
– The filter models the behavior of the vocal tract.
Random
Excitation
Digital Filter
Pitch
Information
Periodic
Excitation
Vocal Tract Filter
Parameters
Synthetic
Speech
Audio Applications of DSP (5/6)
44
3. SPEECH RECOGNITION
– Voice recognition involves inputting of information
into a computer using human voice, and the
computer listening and recognizing the human
speech
– Training MODE: The user trains the system to
recognize his or her voice by speaking each word
to be recognized.
• The system digitizes and creates a template for
each word to be recognized, and stores this in
its memory.
– Recognition MODE: Each spoken word is digitized,
its template compared with the stored template.
• If a match occurs, a pre-defined action is taken.
Audio Applications of DSP (6/6)
45
4. THE COMPACT DISC (CD) DITIGAL AUDIO
SYSTEM
– In the CD, the information is recorded in the digital
form as spiral tracks that consists of a succession
of pits. Each bit recorded on the CD occupies an
area of 1 mic.meter.sq, that is 106 bits/mm2
 Very high density of information on the CD.
Telecommunication Applications of DSP (1/3)
1. MOBILE COMMUNICATION
– Mobile cellular radio telephony systems, such as
GSM employ digital technology, and so DSP is the
natural choice for processing and conveying
information.
– In mobile radio telephony, DSP finds use in
•
•
•
•
•
•
Speech Coding
Multipath Equalization
Signal Strength and Qualioty Measurement
Voice Messaging
Error Control Coding
Modulation / Demodulation
46
Telecommunication Applications of DSP (2/3)
47
2. DIGITAL TV RECEPTION
– Digital TV broadcasting promises a great deal to
consumer:
• INTERACTIVITY: play games, access internet, shop, etc.
• MORE CHOICE: have instant replays, e.g.,
• BETTER SOUND AND PICTURE QUALITY
– The heart of digital TV is MPEG coding algorithms
which are used to compress video/audio
information before transmission (to make good use
of bandwidth)
– Most existing TV sets in our homes can only
receive analog transmission, and so require a
digital recorder to receive digital TV.
Telecommunication Applications of DSP (3/3)
3. ADAPTIVE TELEPHONE ECHO
CANCELLATION
– In long-distance telephone circuit, a 2-wire local
loop is connected to a 4-wire trunk lines using a
hybrid.
– Due to impedance mismatching at hybrid, echos
are generated in a 4-wire loop.
– The solution is an echo canceller, which is based
on ADAPTIVE FILTERING.
48
Biomedical Applications of DSP (1/4)
49
1. MEDICAL IMAGING
– Before DSP the medical X-ray imaging was limited
by four problems:
1. Overlapping structures in the body can hide behind each
other, e.g., the portion of heart might not be visible
behind the ribs.
2. It is not always possible to distinguish between similar
tissues, e.g., it may be able to separate bones from soft
tissues, but not distinguish a tumor from liver
3. X-ray images show anatomy, the body’s structure, and
not physiology, the body’s operation  The X-ray image
of a living person and looks exactly like the X-ray image
of a dead one
4. X-ray exposure can cause cancer, requiring it to be used
sparingly and only with proper justification.
Biomedical Applications of DSP (2/4)
50
1. MEDICAL IMAGING
– Computer Tomography (CT Scan)
•
In computer Tomography X-rays from many directions
are passed through a section of the patient’s body, being
examined.
•
The signals are converted into digital data and stored in
a computer.
•
The information is then used to calculate images that
appear to be slices through the body. The images show
much greater detail allowing significantly better
diagnosis and treatment.
Biomedical Applications of DSP (3/4)
51
1. MEDICAL IMAGING
The last three x-ray problems have been solved by
using penetrating energy other than x-rays, such as
radio and sound waves.
– Magnetic Resonance Imaging (MRI)
•
The MRI uses magnetic fields in conjunction with radio
waves to probe the interior of the human body.
•
Properly adjusting the strength and frequency of the
fields cause the atomic nuclei in a localized region of the
body to resonate between quantum energy states. This
resonance results in the emission of a secondary radio
wave, detected with an antenna placed near the body.
Biomedical Applications of DSP (4/4)
52
1. MEDICAL IMAGING
– Magnetic Resonance Imaging (MRI)
•
•
The strength and other characteristics of this detected
signal provide information about the localized region in
resonance.
This information is usually presented as images, just as in
computed tomography. Besides providing excellent
discrimination between different types of soft tissue, MRI can
provide information about physiology, such as blood flow
through arteries. MRI relies totally on Digital Signal
Processing techniques, and could not be implemented
without them.
2. FETAL ECG ANALYSIS
– To measure the heart-beat rate of fetus in
mother’s womb, DSP is used to remove the
strong interference caused by mother’s heartbeat.