Digital signal Processing ECI-3-832

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Telecommunication and Internet Engineering, School of Engineering, South Bank University. Digital signal Processing ECI-3-832. Semester 1 2003 /2004. Coordinator. Dr. Z. Zhao Room: T409 Tel: 0207 815 6340 Email: [email protected] Textbook.

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South Bank University

### Digital signal ProcessingECI-3-832

Semester 1 2003/2004

Coordinator
Textbook
• Alan V. Oppenheim, Ronald W. Schafer, Discrete-time Signal Processing, 2ed, Prentice Hall, ISBN: 0-13-083443-2
Unit Structure
• 1.    Introduction to DSP
• 2.    Discrete-time signals
• 3.    Discrete-time systems
• 4.    The z-transform and the Fourier transforms of discrete-time signals
• 5.    The discrete Fourier transform (DFT) and its efficient computation (FFT)
• 6.    Digital filters
Unit Calendar

(Changes possible)

• Introduction to DSP 1
• Discrete-time signals 1-2
• Discrete-time systems 3-4
• The z-transform and the Fourier transforms 5-7

of discrete-time signals

• The discrete Fourier transform (DFT) and 8-10

its efficient computation (FFT)

• Digital filters 12
• Revision 13
• Examination 14-15
Teaching and Learning Methods
• Lecture: 2 hour each week
• Tutorial: 2 hour on Even weeks
• Laboratory work (Matlab exercises):2 hour of on odd weeks
• Self learning: 102 hours
Assessment
• 3-hour written examination: 75%
• Workshop assignment: 25%

1. log book

2. formal written reports

3. Submit: J200 between 10:00 and 16:00, following the standard school procedure.

Introduction to DSP

1.1 What is DSP?

DSP, or Digital Signal Processing,

is concerned with the use of programmable digital hardware and software (digital systems) to perform mathematical operations on a sequence of discrete numbers (a digital signal).

Anti-aliasing filter

DSP

A/D

Reconstruction filter

D/A

Introduction to DSP

1.2 A General DSP System

Analog

signal

Analog

signal

Digital

signal

Digital

signal

Analog

signal

Analog

signal

Introduction to DSP

• Programmable
• Well-defined, stable, and repeatable
• Manipulating data in the digital domian provides high immunity from noise
• Use of computer algorithms allows implementation of functions and features that are impossible with analog methods
Introduction to DSP

• Relatively low bandwidths
• Signal resolution is limited by the D/A and A/D converters.
Introduction to DSP

1.5 Applications:

• digital sound recording such as CD and DAT
• speech and compression for telecommucation and storage
• implementation of wireline and radio modems
• image enhancement and compression
• speech synthesis and speech recognition
What is DSP Used For?

…And much more!

Word

pronunciation

Phoneme

models

Semantic

knowledge

Feature

extraction

Phoneme

recognition

Word

recognition

Sentence

recognition

speech

decision

Dialogue

knowledge

Syntactic

knowledge

Speech Recognition System

To be or

not to be

that is the

question

Input

text

Tu bee awr

nawt tu bee

dhat iz dhe

kwestchun

phonetic form

Text

normalization

Parsing

Pronunciation

semantic &

syntactic ‘parts

of speech’

analysis of text

phonetic description

of each word, dictionary

with letter-to-sound

rules as a back up

expands

abbreviations

dates, times,

money..etc

Prosody

rules

Waveform

generation

Synthesized

speech

Apply word

stress, duration

and pitch

Phonetic-to-

acoustic

transformation

Text-to-Speech Synthesis

Encoder

Original Speech

• Analysis:
• Voiced/Unvoiced decision
• Pitch Period (voiced only)
• Signal power (Gain)

Decoder

PitchPeriod

Signal Power

Pulse Train

V/U

Vocal TractModel

Synthesized Speech

LPC-10:

Random Noise

Speech Coding – Vocoder

Original

JPEG (4:1)

JPEG (100:1)

JPEG Example