Multimedia Signal Processing 1 Module Fundamentals of ...marconlab.deib.polimi.it/courses/MMSP/slides/00... · Some fundamentals of Analog Signal Processing will be recalled (sampling,

Marco Marcon

Politecnico di Milano

Dipartimento di Elettronica,Informazione e Bioingegneria (DEIB)

Multimedia Signal Processing1st Module

Fundamentals of Multimedia Signal Processing

Course overviewThis module covers the fundamental tools for digital signal processing. In particular it addresses topics of signal analysis and filtering concerning audio and video data.Some aspects of Statistical Signal Processing will also be presented during the course.Participants will learn basic tools for digital signal filtering and analysis acquiring the knowledge to design specific filters and . In particular the program will be articulated in the following parts:

Review of Analog Signal Processing theory: Some fundamentals of Analog Signal Processing will be recalled (sampling,

quantization, Fourier Transform and Series, noise description...). Introduction to Discrete Signals Transforms:

The z-transform, the Discrete Time Fourier Transform and the Discrete Fourier Transform will be described with their properties and application to filter design and analysis.

Introduction to Digital Filters: This course part will cover Time-domain filter representations, transfer function

analysis, frequency response analysis, Finite and Infinite impulse response implementation; stability analysis.

Course overview Windowing and Short Time Fourier Transform:

Overview of windows and real-time processing; overlap and add, overlap and save, Short Time Fourier Transform for real time processing

Introduction to Multirate Processing: Downsampling, upsampling, polyphase filters, perfect reconstruction filter banks.

Elements of Statistical Signal Processing: Random sequences, properties and characterization. Spectral estimation, the

Periodogram, linear prediction. Introduction to estimation theory, power spectral density, parametric and nonparametric spectral estimation. Autocorrelation and autocovariance methods.

Laboratory activities Laboratory activities enable student to improve their understanding of the concepts

learnt during the lectures. The proposed examples of applications and exercises, cover audio, image and video

processing, and are based on Matlab® and the related toolboxes (Digital Signal Processing System Toolbox, Audio System Toolbox, Image acquisition toolbox, Image Processing Toolbox ).

Examples of real-time processing with embedded devices (Raspberry Pi) and Simulink will also be provided.

Course overview Laboratory activities

Laboratory activities enable student to improve their understanding of the concepts learnt during the lectures.

The proposed examples of applications and exercises are based on Matlab® and Simulink ®

Website marcon.net

E-mail [email protected]

Skype marco.marcon

Suggested books “Digital Signal Processing: System Analysis and

Design”, Paulo S. R. Diniz, Eduardo A. B. da Silva, Sergio L. Netto

“Lecture notes”, Marco Tagliasacchi Available on the website.

Matlab – Signal Processing Toolbox guide http://www.mathworks.it/help/toolbox/signal/index.html

DAFX: Digital Audio Effects, 2nd Edition, Editor: Udo Zolzer

Further Italian book suggested for Italian students not too confident with English: “Elaborazione Numerica dei Dati”, Massimiliano Laddomada – Marina Mondin

http://www.mathworks.it/help/toolbox/signal/index.html

Signals Signals are functions of independent variables that carry

information. For example:• Electrical signals --- voltages and currents in a circuit• Acoustic signals --- audio or speech signals (analog or digital)

Spectrogram

Time-domainSpeech signal

Signals• Video signals --- intensity variations in an image (e.g.

a CAT scan)

• Biological signals --- sequence of bases in a geneDNA

Its signal representation

• For electrical signal the value of voltage or current changes with time, hence time is called independent variable and voltage or current is called dependent variable

• Independent variable can be continuous— Trajectory of a space shuttle— Mass density in a cross-section of a brain

• Independent variable can be discrete— DNA base sequence— Digital image pixels

• Independent variable can be 1-D, 2-D, ••• N-D

8

What are the independent variables in these signals?(i) Speech (ii) CAT scan image (iii) DNA sequence(i) Time (ii) Spatial Location (iii) Location on DNA molecule

For this course: Focus on a single (1-D) independent variable which we call “time”.

Continuous-Time (CT) signals: x(t), t — continuous valuesDiscrete-Time (DT) signals: x[n], n — integer values only

9

• Most of the signals in the physical world are CT signals—E.g. voltage & current, pressure, temperature, velocity, etc.

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• Examples of DT signals in nature:— DNA base sequence— Population of the n-th generation of certain species

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• x[n], n — integer, time varies discretely

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Ex.#1 Weekly Dow-Jonesindustrial average

Why DT? —Can be processed by modern digital computers

and digital signal processors (DSPs).

Ex.#2 digital image

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For the most part, our view of systems will be from an input-output perspective:A system responds to applied input signals, and its response is described in terms of one or more output signals

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• Dynamics of an aircraft or space vehicle• An algorithm for analyzing financial and economic

factors to predict bond prices• An algorithm for post-flight analysis of a space launch• An edge detection algorithm for medical images

What are the inputs and what are the outputs in above examples?

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• An important concept is that of interconnecting systems— To build more complex systems by interconnecting

simpler subsystems— To modify response of a system

• Signal flow (Block) diagram

Cascade

Feedback

Parallel +

+

Moving from Analog to Digital World What is DSP? Converting Analog into Digital

Electronically Computationally

How Does It Work? Faithful Duplication Resolution Trade-offs

What is DSP? Converting a continuously changing waveform

(analog) into a series of discrete levels (digital)

What is DSP? The analog waveform is sliced into equal segments and

the waveform amplitude is measured in the middle of each segment

The collection of measurements make up the digital representation of the waveform

What is DSP?0

0.22 0

.44 0.

64 0.8

2 0.98 1.

11 1.2 1.24

1.27

1.24

1.2

1.11

0.98

0.82

0.64

0.44

0.22

0-0

.22

-0.4

4-0

.64

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2-0

.98

-1.1

1-1

.2-1

.26

-1.2

8-1

.26

-1.2

-1.1

1-0

.98

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2 -0.6

4 -0.4

4 -0.2

20

-2

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-1

-0.50

0.5

1

1.5

21 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37

Converting Analog into DigitalElectronically The device that does the conversion is called an

Analog to Digital Converter (ADC) There is a device that converts digital to analog that is

called a Digital to Analog Converter (DAC)

Converting Analog into DigitalElectronically The simplest form of

ADC uses a resistance ladder to switch in the appropriate number of resistors in series to create the desired voltage that is compared to the input (unknown) voltage

V-7

V-6

V-low

V-1

V-2

V-3

V-4

V-5

V-high

SW-8

SW-7

SW-6

SW-5

SW-4

SW-3

SW-2

SW-1

Output

Converting Analog into DigitalElectronically The output of the

resistance ladder is compared to the analog voltage in a comparator

When there is a match, the digital equivalent (switch configuration) is captured

Analog Voltage

ResistanceLadder Voltage

ComparatorOutput Higher

EqualLower

Converting Analog into DigitalComputationally The analog voltage can now be compared with the

digitally generated voltage in the comparator Through a technique called binary search, the

digitally generated voltage is adjusted in steps until it is equal (within tolerances) to the analog voltage

When the two are equal, the digital value of the voltage is the outcome

Converting Analog into DigitalComputationally The binary search is a mathematical technique

that uses an initial guess, the expected high, and the expected low in a simple computation to refine a new guess

The computation continues until the refined guess matches the actual value (or until the maximum number of calculations is reached)

The following sequence takes you through a binary search computation

Binary Search Initial conditions

Expected high 5-volts Expected low 0-volts 5-volts 256-binary 0-volts 0-binary

Voltage to be converted 3.42-volts Equates to 175 binary

Analog Digital5-volts

256

0-volts 0

2.5-volts

128

3.42-volts

Unknown (175)

Binary Search Binary search algorithm:

First Guess:

Analog Digital5-volts

256

0-volts 0

128

3.42-volts

unknownNewGuessLowLowHigh =+−

2

12802

0256=+

−

Guess is Low

Binary Search New Guess (2): Analog Digital

5-volts

256

0-volts 0

1923.42-volts

unknown192128

2128256

=+−

Guess is High


5-volts

256

0-volts 0

1603.42-volts

unknown160128

2128192

=+−

Guess is Low


5-volts

256

0-volts 0

1763.42-volts unknown176160

2160192

=+−

Guess is High


5-volts

256

0-volts 0

1683.42-volts

unknown168160

2160176

=+−

Guess is Low


5-volts

256

0-volts 0

1723.42-volts

unknown172168

2168176

=+−

Guess is Low

(but getting close)


5-volts

256

0-volts 0

1743.42-volts

unknown174172

2172176

=+−

Guess is Low

(but getting really, really, close)


5-volts

256

0-volts 0

3.42-volts

175!175174

2174176

=+−

Guess is Right On

Binary Search The speed the binary search is accomplished depends

on: The clock speed of the ADC The number of bits resolution Can be shortened by a good guess (but usually is not

worth the effort)

How Does It Work?Faithful Duplication Now that we can slice up a waveform and convert it

into digital form, let’s take a look at how it is used in DSP

Draw a simple waveform on graph paper Scale appropriately

“Gather” digital data points to represent the waveform

Starting Waveform Used to Create Digital Data

How Does It Work?Faithful Duplication Swap your waveform data with a partner Using the data, recreate the waveform on a sheet of

graph paper

Waveform Created from Digital Data

How Does It Work?Faithful Duplication

Compare the original with the recreating, note similarities and differences

How Does It Work?Faithful Duplication Once the waveform is in digital form, the real power of

DSP can be realized by mathematical manipulation of the data

Using EXCEL spreadsheet software can assist in manipulating the data and making graphs quickly

Let’s first do a little filtering of noise

How Does It Work?Faithful Duplication Using your raw digital data, create a new table of data

that averages three data points Average the point before and the point after with the

point in the middle Enter all data in EXCEL to help with graphing

Noise Filtering Using Averaging

Raw

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TimeRawAve before/aftereliminate extremes (100/-100)

0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

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30-38-33-38

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33252225

34433843

35454945

36585858

37706970

38806880

39556255

40503550

41000

Timeevery 2ndTimeevery 3rdTimeevery 6thTimeevery 12

00000000

110250612512-110

35557812-1102490

57881018453658

712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

231083545

25-83880

27-55410

29-45

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3325

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3770

3955

410

Sheet1

Raw

Time

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Ave before/after

Time

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Sheet3

eliminate extremes (100/-100)

Time

Amplitude

every 2nd

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every 6th

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every 3rd

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every 12

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every 12th

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38.3333333333

60

69.3333333333

92.6666666667

108.3333333333

85.6666666667

37.3333333333

-19.3333333333

-56

-86

-113.3333333333

-105

-98.3333333333

-65

-40

5

41.6666666667

71.6666666667

90

96.6666666667

102.6666666667

99.3333333333

63.3333333333

14

-34.3333333333

-58.3333333333

-60

-54.3333333333

-32.6666666667

-18.3333333333

2.6666666667

22

37.6666666667

48.6666666667

57.6666666667

69.3333333333

68.3333333333

61.6666666667

35

0

Sheet1


0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

32-23-2

33252225

34433843

35454945

36585858

37706970

38806880

39556255

40503550

41000


00000000

110250612512-110

35557812-1102490

57881018453658

712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

231083545

25-83880

27-55410

29-45

31-15

3325

3545

3770

3955

410

Sheet1

Raw

Time

Amplitude

Sheet2

Ave before/after

Time

Amplitude

Sheet3


Time

Amplitude

every 2nd

Time

Amplitude

every 6th

Time

Amplitude

every 3rd

Time

Amplitude

every 12

Time

Amplitude

every 12th

How Does It Work?Faithful Duplication Let’s take care of some static crashes that cause some

interference Using your raw digital data, create a new table of data

that replaces extreme high and low values: Replace values greater than 100 with 100 Replace values less than -100 with -100

Clipping of Static Crashes

Raw

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0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

32-23-2

33252225

34433843

35454945

36585858

37706970

38806880

39556255

40503550

41000


00000000

110250612512-110

35557812-1102490

57881018453658

712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

231083545

25-83880

27-55410

29-45

31-15

3325

3545

3770

3955

410

Sheet1

Raw

Time

Amplitude

Sheet2

Ave before/after

Time

Amplitude

Sheet3


Time

Amplitude

every 2nd

Time

Amplitude

every 6th

Time

Amplitude

every 3rd

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Amplitude

every 12

Time

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every 12th

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0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

32-23-2

33252225

34433843

35454945

36585858

37706970

38806880

39556255

40503550

41000


00000000

110250612512-110

35557812-1102490

57881018453658

712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

231083545

25-83880

27-55410

29-45

31-15

3325

3545

3770

3955

410

Sheet1

Raw

Time

Amplitude

Sheet2

Ave before/after

Time

Amplitude

Sheet3


Time

Amplitude

every 2nd

Time

Amplitude

every 6th

Time

Amplitude

every 3rd

Time

Amplitude

every 12

Time

Amplitude

every 12th

How Does It Work?Resolution Trade-offs Now let’s take a look at how sampling rates affect the

faithful duplication of the waveform Using your raw digital data, create a new table of data

and delete every other data point This is the same as sampling at half the rate

Half Sample Rate

Raw

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0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

32-23-2

33252225

34433843

35454945

36585858

37706970

38806880

39556255

40503550

41000


00000000

110250612512-110

35557812-1102490

57881018453658

712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

231083545

25-83880

27-55410

29-45

31-15

3325

3545

3770

3955

410

Sheet1

Raw

Time

Amplitude

Sheet2

Ave before/after

Time

Amplitude

Sheet3


Time

Amplitude

every 2nd

Time

Amplitude

every 6th

Time

Amplitude

every 3rd

Time

Amplitude

every 12

Time

Amplitude

every 12th

Chart9

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every 2nd

Time

Amplitude

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-90

0

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100

108

-8

-55

-45

-15

25

45

70

55

0

Sheet1


0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

32-23-2

33252225

34433843

35454945

36585858

37706970

38806880

39556255

40503550

41000


00000000

110250612512-110

35557812-1102490

57881018453658

712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

231083545

25-83880

27-55410

29-45

31-15

3325

3545

3770

3955

410

Sheet1

Raw

Time

Amplitude

Sheet2

Ave before/after

Time

Amplitude

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Time

Amplitude

every 2nd

Time

Amplitude

every 6th

Time

Amplitude

every 3rd

Time

Amplitude

every 12

Time

Amplitude

every 12th

How Does It Work?Resolution Trade-offs Using your raw digital data, create a new table of data

and delete every second and third data point This is the same as sampling at one-third the rate

1/2 Sample Rate

Raw

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Sheet1


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1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

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19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

32-23-2

33252225

34433843

35454945

36585858

37706970

38806880

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40503550

41000


00000000

110250612512-110

35557812-1102490

57881018453658

712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

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27-55410

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Time

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Time

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every 2nd

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every 6th

Time

Amplitude

every 3rd

Time

Amplitude

every 12

Time

Amplitude

every 12th

Chart10

0

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5

8

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26

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every 3rd

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78

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90

108

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0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

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33252225

34433843

35454945

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37706970

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40503550

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57881018453658

712211-1002490410

9-2014-7530-38

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15-9023108

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198029-45

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231083545

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Time

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Sheet3


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every 2nd

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every 6th

Time

Amplitude

every 3rd

Time

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every 12

Time

Amplitude

every 12th


and delete all but every sixth data point This is the same as sampling at one-sixth the rate

1/6 Sample Rate

Raw

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-48

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0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

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16-30-40-30

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20909090

2110097100

22100103100

2310899100

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Time

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every 2nd

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every 6th

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Amplitude

every 3rd

Time

Amplitude

every 12

Time

Amplitude

every 12th

Chart11

0

6

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18

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every 6th

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Amplitude

0

125

-110

45

90

-38

58

0

Sheet1


0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

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34433843

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38806880

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40503550

41000


00000000

110250612512-110

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712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

231083545

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Amplitude

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Time

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Sheet3


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every 2nd

Time

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every 6th

Time

Amplitude

every 3rd

Time

Amplitude

every 12

Time

Amplitude

every 12th


and delete all but every twelfth data point This is the same as sampling at one-twelfth the rate

1/12 Sample Rate

Raw

-150

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0

50

100

150

0 10 20 30 40

Time

Ampl

itude

every 12th

-150

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Ampl

itude

Chart5

0

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Raw

Time

Amplitude

0

10

50

55

75

78

125

122

10

-20

-48

-100

-110

-130

-75

-90

-30

0

45

80

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100

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108

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-55

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25

43

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70

80

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50

0

Sheet1


0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

32-23-2

33252225

34433843

35454945

36585858

37706970

38806880

39556255

40503550

41000


00000000

110250612512-110

35557812-1102490

57881018453658

712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

231083545

25-83880

27-55410

29-45

31-15

3325

3545

3770

3955

410

Sheet1

Raw

Time

Amplitude

Sheet2

Ave before/after

Time

Amplitude

Sheet3


Time

Amplitude

every 2nd

Time

Amplitude

every 6th

Time

Amplitude

every 3rd

Time

Amplitude

every 12

Time

Amplitude

every 12th

Chart12

0

12

24

36

41

every 12

Time

Amplitude

every 12th

0

-110

90

58

0

Sheet1


0000

1102010

2503850

3556055

4756975

5789378

6125108100

712286100

8103710

9-20-19-20

10-48-56-48

11-100-86-100

12-110-113-100

13-130-105-100

14-75-98-75

15-90-65-90

16-30-40-30

17050

18454245

19807280

20909090

2110097100

22100103100

2310899100

24906390

25-814-8

26-40-34-40

27-55-58-55

28-80-60-80

29-45-54-45

30-38-33-38

31-15-18-15

32-23-2

33252225

34433843

35454945

36585858

37706970

38806880

39556255

40503550

41000


00000000

110250612512-110

35557812-1102490

57881018453658

712211-1002490410

9-2014-7530-38

11-1001703658

13-1302090410

15-9023108

17026-40

198029-45

2110032-2

231083545

25-83880

27-55410

29-45

31-15

3325

3545

3770

3955

410

Sheet1

Raw

Time

Amplitude

Sheet2

Ave before/after

Time

Amplitude

Sheet3


Time

Amplitude

every 2nd

Time

Amplitude

every 6th

Time

Amplitude

every 3rd

Time

Amplitude

every 12

Time

Amplitude

every 12th

How Does It Work?Resolution Trade-offs What conclusions can you draw from the changes in

sampling rate? At what point does the waveform get too corrupted by

the reduced number of samples? Is there a point where more samples does not appear to

improve the quality of the duplication?

How Does It Work?Resolution Trade-offs

Bit Resolution

High Bit Count

Good Duplication

Slow

Low Bit Count

Poor Duplication

Fast

Sample Rate High Sample Rate

Good Duplication

Slow

Low Sample Rate

Poor Duplication

Fast

WHERE is DSP?A signal is any variable that carries information.

Examples of the types of signals of interest are: Speech (telephony, radio, everyday communication) Biomedical signals (EEG brain signals) Sound and music Video and image Radar signals (range and bearing).Digital signal processing (DSP) is concerned with the

digital representation of signals and the use of digital processors to analyze, modify, or extract information from signals.

WHY DSP?Many signals in DSP are derived from analogue signals which have

been sampled at regular intervals and converted into digital form. The key advantages of DSP over analogue processing are:

Guaranteed accuracy (determined by the number of bits used) Perfect reproducibility No drift in performance due to temperature or age Takes advantage of advances in semiconductor technology Greater flexibility (can be reprogrammed without modifying

hardware) Superior performance (linear phase response possible, and filtering algorithms can be made adaptive)

Sometimes information may already be in digital form.

DSP disadvantages Speed and cost (DSP design and hardware may be

expensive, especially with high bandwidth signals) Finite wordlength problems (limited number of bits

may cause degradation).

DSP application areas Image processing (pattern recognition, robotic vision,

image enhancement, facsimile, satellite weather map, animation)

Instrumentation and control (spectrum analysis, position and rate control, noise reduction, data compression)

Speech and audio (speech recognition, speechsynthesis, text to speech, digital audio, equalisation)

Military (secure communication, radar processing, sonar processing, missile guidance)

DSP application areas (cont.) Telecommunications (echo cancellation, adaptive

equalisation, spread spectrum, video conferencing, data communication)

Biomedical (patient monitoring, scanners, EEG brainmappers, ECG analysis, X-ray storage and enhancement).

An example: CDs Information on a compact disk is recorded on a spiral

track as a succession of pits (106 bits/mm2). The recording or mastering process is depicted below:

CD Recording process Analogue signal in each stereo channel is sampled at

44.1 kHz and digitised to 16 bits (96 dB dynamic range), resulting in 32 bits per sampling instant.

Encoded using a two-level Reed-Solomon code to enable errors to be corrected or concealed during reproduction.

An EFM (eight-to-fourteen) modulation scheme translates each byte in the stream to a 14 bit code, which is more suitable for disc storage (eliminates adjacent 1’s, etc.)

The resulting bit stream is used to control a laser beam, which records information on the disc.

CD audio signal reconstruction

CD Audio signal reconstruction (cont.) Track optically scanned at 1.2 m/s Signal is demodulated, errors detected and (if

possible) corrected. If correction is not possible, errors are concealed by interpolation or muting.

This results in a series of 16 bit words, each representing a single audio sample. These samples could be applied directly to a DAC and analogue lowpass filtered

CD Audio signal reconstruction (cont.) However, this would require high specification lowpassfilters (20 kHz frequencies must be reduced by 50 dB), and the filter should have linear phase. To avoid this, signals are upsampled by a factor of 4. This makes the output of the DAC smoother, simplifying the analogue filtering requirements.

The use of a digital filter also allows a linear phase response, reduces chances of intermodulation, and yields a filter that varies with clock rate.

Multimedia Signal Processing�1st Module�Fundamentals of Multimedia Signal ProcessingCourse overviewCourse overviewCourse overviewSuggested booksSignalsSignalsTHE INDEPENDENT VARIABLESTHE INDEPENDENT VARIABLESCT SignalsDT SignalsMany human-made DT SignalsSYSTEMSEXAMPLES OF SYSTEMSSYSTEM INTERCONNECTIONSMoving from Analog to Digital WorldWhat is DSP?What is DSP?What is DSP?Converting Analog into Digital�ElectronicallyConverting Analog into Digital�ElectronicallyConverting Analog into Digital�Electronically�Converting Analog into Digital�ComputationallyConverting Analog into Digital�ComputationallyBinary SearchBinary SearchBinary SearchBinary SearchBinary SearchBinary SearchBinary SearchBinary SearchBinary SearchBinary SearchHow Does It Work?�Faithful DuplicationStarting Waveform Used to Create Digital DataHow Does It Work?�Faithful DuplicationWaveform Created from Digital DataHow Does It Work?�Faithful DuplicationHow Does It Work?�Faithful DuplicationHow Does It Work?�Faithful DuplicationNoise Filtering Using AveragingHow Does It Work?�Faithful DuplicationClipping of Static CrashesHow Does It Work?�Resolution Trade-offsHalf Sample RateHow Does It Work?�Resolution Trade-offs1/2 Sample RateHow Does It Work?�Resolution Trade-offs1/6 Sample RateHow Does It Work?�Resolution Trade-offs1/12 Sample RateHow Does It Work?�Resolution Trade-offsHow Does It Work?�Resolution Trade-offsWHERE is DSP?WHY DSP?DSP disadvantagesDSP application areasDSP application areas (cont.)An example: CDsCD Recording processCD audio signal reconstructionCD Audio signal reconstruction (cont.)CD Audio signal reconstruction (cont.)

Documents

Multimedia Signal Processing 1 Module Fundamentals of ...marconlab.deib.polimi.it/courses/MMSP/slides/00... · Some fundamentals of Analog Signal Processing will be recalled (sampling,