P01 - Pengantar Digital Signal Processing

7/25/2019 P01 - Pengantar Digital Signal Processing

1/47

Digital Signal Processing:An Introduction and Some Examples of its

Everyday Use

Ardik Wijayanto

[email protected]


2/47

Target

Sampling

FFT

Filter

Slide 2


3/47

Materi Dalam 1 Semester

Pendahuluan (1 TM) Dasar DSP : Sinyal dan Sistem (2 TM) + 1 Tugas

ADC dan Segala Aspeknya (1 TM)

Persamaan Beda Koef Linier Konstan (2 TM)

Konvolusi (1 TM) + 1 Tugas

Transformasi Laplace (1 TM)

Transformasi Z (1 TM) Filter Digital dan Segala Aspeknya (2 TM) + 1 Tugas

Transformasi Fourier (1 TM) + 1 Tugas

TM = Tatap Muka


4/47

DSP ???


5/47

Slide 5

Contents

What is DSP?

What is DSP used for?

Speech & Audio processing

Image & Video processing Adaptive filtering

DSP Devices and Architectures

Summary & Conclusions


6/47

Slide 6

What is DSP?

Digital Signal Processing the processingor manipulation of signals using digital

techniques

ADC DAC

Digital

Signal

ProcessorAnalogueto Digital

Converter

Digital to

Analogue

Converter

Input

SignalOutput

Signal


7/47

PSD - Ardik / Bima


8/47

PSD - Ardik / Bima


9/47

PSD - Ardik / Bima


10/47

Slide 10

What is DSP Used For?

And much more!


11/47

Slide 11


12/47

Slide 12

The world is filled withsignals

1-D signals:

Seismic vibrations

EEG and EKG

Speech

Sonar

Audio & music

Dow-Jones averages

2-D signals: Photographs

Medical images

Radar

Satellite data Fax

Fingerprints

And of course there are 3-d signals (video, volumetric data sets)

and beyond


13/47

Slide 13

And we want toprocessthem

Recognizewhats in a signal Target detection

Speech recognition

Image analysis

Predicta future value of thesignal

Stock market prediction Interpolatemissing values

of a signal Conceal lost video packets

Restorea signal that hasbeen degraded

Noise removal Echo cancellation

Enhancea signal Image contrast enhancement

Compressa signal Faster transmission

Less storage space

Synthesizea realistic example ofa signal Speech synthesis

Image texture generation

Choosespecific input signals tocontrol a process Chemical process control

Aerodynamic control


14/47

PSD - Ardik / Bima


15/47

Slide 15


16/47

Slide 16

Speech Processing

Speech coding/compression

Speech synthesis

Speech recognition


17/47

Slide 17

Some Properties of Speech

The blue--- s---p--o---------t i-s--on--the-- k--ey a---g--ai----n------

oo in blueo in spotee in keye in agains in spotk in key


18/47

Slide 18

Some Properties of Speech

ee in keyo in spotoo in blue e in again

Vowels

s in spot k in key

Consonants

Quasi-periodic

Relatively high signal power

Non-periodic (random)

Relatively low signal power


19/47

Slide 19

Speech Coding

BTS

BSC

MSC

TRAU

64 kbits/s

13 kbits/s

22.8 kbits/s


20/47

Slide 20

Speech Coding Linear Prediction

A(z)

s(n)+

d(n)

se(n)

d(n)

A(z)

++

se(n)

sr(n)

Try topredictthe current sample value; Transmit theprediction error.


21/47

Slide 21

Speech Coding Vocoder

G

Pulse Train

Random Noise

Vocal Tract

Model

V/U

Synthesized Speech

Decoder

Original Speech

Analysis:

Voiced/Unvoiced decision

Pitch Period (voiced only)

Signal power (Gain)

Signal Power

Pitch

Period

Encoder

LPC-10:


22/47

Slide 22

To be or

not to bethat is thequestion

Textnormalization

expands

abbreviationsdates, times,money..etc

Parsing Pronunciation

Prosodyrules

Tu bee awrnawt tu beedhat iz dhekwestchun

semantic &

syntactic partsof speechanalysis of text

phonetic description

of each word, dictionarywith letter-to-soundrules as a back up

Waveformgeneration

Synthesizedspeech

Apply word

stress, durationand pitch

Phonetic-to-

acoustictransformation

phonetic form

Inputtext

Text-to-Speech Synthesis

Text-to-speech synthesis sounds very natural these days.


23/47

Slide 23

Speech Synthesis Applications

Speaking clocks

Spoken (variable) announcements

Talking emails + talking heads for mobile

Synthesis of location-based information(e.g. traffic information)

Interactive systems (e.g. catalogue ordering,

Yellow Pages, ...)


24/47

Slide 24

Speech/Speaker Recognition

Speech Recognition What has been spoken?

Speaker dependent Recognition system trained

for a particular persons voice.

Speaker independent Recognition system

expected to deal with a wide variety of speakers.

Speaker Recognition Who has spoken?

Not easy

Sometimestherearenogapsbetweenwords.

Sometim esthereareg aps inthe mid dleofwords.

Accents, dialects and Stress eggsist.


25/47

Slide 25

Speech Recognition System

Featureextractionspeech Phonemerecognition

Phonememodels

SentencerecognitionWordrecognition

Wordpronunciation

Semanticknowledge

decision

Syntacticknowledge

Dialogueknowledge


26/47

Slide 26

Digital Audio

Standard music CD:

Sampling Rate: 44.1 kHz

16-bit samples 2-channel stereo

Data transfer rate = 21644,100 = 1.4 Mbits/s

1 hour of music = 1.43,600 = 635 MB


27/47

Slide 27

Audio Coding (Contd)

Key standards: MPEG: Layers I, II, and III (MP3); AAC.

used in DAB, DVD

Dolby AC3, Dolby Digital, Dolby Surround.

Typical bit rates for 2-channel stereo:

64kbits/s to 384 kbits/s.

Subband- or transform-based, making use

of perceptual masking properties.


28/47

Slide 28

Audio Coding (Contd)

5.1 channels (3/2) with LFE channel:

Left, Right, Centre,

Left Surround, Right Surround,

Low Frequency Effects (LFE) (Reduced Bandwidth). LFE loudspeaker can, in general, be placed anywhere in the

listening room.

Typical 3/2 multichannel stereo configuration:

RightSurround

Right

Surround

Left

Centre

Left


29/47

Slide 29

Audio Coding Masking

Auditory Masking: Spectral: Strong frequency components mask weaker

neighbouring frequency components.

Temporal: Strong temporal events mask recent and

future events.

SPL/dB

10ms 160ms

Temporal Masking

timefreq/kHz1

SPL/dB

Spectral Masking


30/47

Slide 30

200 300 400 500 600 700 800

10

20

30

40

50

60

Hz

dB

Masking Example


31/47

Slide 31

Image/Video

Still Image Coding: JPEG (Joint Photographic Experts Group):

Discrete Cosine Transform (DCT) based

JPEG2000: Wavelet Transform based

Video Coding:

MPEG (Moving Pictures Experts Group):

DCT-based,

Interframe and intraframe prediction,

Motion estimation.

Applications: Digital TV, DVD, etc.


32/47

Slide 32

JPEG Example

Original

JPEG (100:1)JPEG (4:1)

Video compression: Example of a


33/47

Slide 33

Video compression: Example of a

packet loss & need to interpolate:

Packet loss means

a horizontal slice of

data is missing

The decoder holds

over the slice fromthe previous frame

to conceal the loss.

Because of camera

pan, slice from

previous framedoesnt line up well

Interpolating the missing data in the current frame using the data from

above & below would likely provide better visual concealment of the loss


34/47

Slide 34

Example: contrast enhancement

Original magnetic resonance

brain scanContrast enhancement using

histogram equalization


35/47

Slide 35

ECE161C: DSP II

Image processing and computer vision

Topics include:

image formation: cameras, radiometry,

and color

2D DSP, Discrete Cosine Transform Filtering, Edge detection

Multiscale representations, texture

Least squares model fitting, motion

Statistics

Principal components and face detection

Video compression

Quantization of 24-bit true color


36/47

Slide 36

Quantization of 24-bit true color

down to 8-bit color:

24-bit color original 8-bit color quantized version

A little random dithering would help mask the quantization banding on the right

ECE 172A Introduction to


37/47

Slide 37

ECE 172A Introduction to

Intelligent Systems

Main topics covered in the courseinclude:

1. Introduction to Intelligent Systems andSensor-based Robots

2. Model-Based approach in perception

3. Image segmentation

4. Edge Detection5. Region growing

6. Texture analysis

7. Object recognition and imageunderstanding

8. Extraction of 3-dimensional cues: passiveand active approaches

Project (about 5 weeks long):1. Vehicle Detection & Re-identification

2. Person Detection and Tracking

3. Robust Image Classification

Input Image

Image Classified as a

Day Image


38/47

Slide 38

Adaptive Filtering

Self-learning: Filter coefficients adapt in responseto training signal.

W(z) +x(n)y(n)

e(n)

d(n)

)()(2)()1( nnenn x

Filter update: Least Mean Squares (LMS)algorithm

d i il i li i


39/47

Slide 39

Adaptive Filtering Applications

Echo cancellation (telephone lines)

Used in modems (making Internet access possible!!)

Acoustic echo cancellation

Hands-free telephony

Adaptive equalization

Active noise control

Medical signal processing

e.g. foetal heart beat monitoring

S O h A li i A


40/47

Slide 40

Some Other Application Areas

Image analysis, e.g: Face recognition, Optical Character Recognition (OCR);

Restoration of old image, video, and audio signals;

Analysis of RADAR data;

Analysis of SONAR data;

Data transmission (modems, radio, echocancellation, channel equalization, etc.);

Storage and archiving;

Control of electric motors.

DSP D i & A hi


41/47

Slide 41

DSP Devices & Architectures

Selecting a DSP several choices: Fixed-point;

Floating point;

Application-specific devices

(e.g. FFT processors, speech recognizers,etc.).

Main DSP Manufacturers:

Texas Instruments (http://www.ti.com)

Motorola (http://www.motorola.com)

Analog Devices (http://www.analog.com)

T i l DSP O ti


42/47

Slide 42

Typical DSP Operations

Filtering Energy of Signal

Frequency transforms

1

0

)()(L

ii inxany

for (n=0; n


43/47

Slide 43

Traditional DSP Architecture

X RAM Y RAM

Multiply/Accumulate

Accumulator

x(n-i) ai

y(n)

N.B. Most modern DSPs have more advanced features.

DSP t EPSON


44/47

Slide 44

DSP at EPSON

EPSON Scotland Design Centre develops a

broad range of technologies to minimize

power consumption and maximize cost

effectiveness in mobile DSP applications.

Energy-saving Firmware

SDC C Skill


45/47

Slide 45

SDC Core Skills

System Integration

System modelling

DSP

Speech Testing

Speech enhancement

Speech synthesis

Speech Recognition

Speech

PerformanceAssessment

MP3

Audio

Channel coding

Basebandprocessing

Mobile

Computer&

Networking

CAD Tools

Administration

Services

System on Chip (SoC)

Firmware design

AMR Coding

Speech compression

CPU (Oak, ARM)

H/w & S/wCo-design

Other digital audio

SDC Firmware Development


46/47

Slide 46

SDC Firmware Development

Algorithm

Definition

Floating-pointand

Fixed-pointCo-Simulation

Co-Design

Implementation Co-Verification

COSSAPMatlab ...

Product Development

Behavioural,RTL, Logic ...

With Barcelona and TokyoDesign Centres

MCU, DSP ...



47/47

Slide 47


DSP used in a wide range of everyday applications Looked at:

Speech coding; Speech synthesis & recognition;

Image/Video;

Adaptive filtering.

Other areas include:

Image analysis (e.g. face recognition, OCR, etc.);

RADAR/SONAR;

Data transmission and reception;

And many more..!!

Documents

P01 - Pengantar Digital Signal Processing