1. Digital signal Processing Digital signal Processing ECI-3-832 Semester 1 2003/2004 Telecommunication and Internet Engineering, School of Engineering, South Bank University

2. Coordinator Dr. Z. Zhao Room: Room: T409 Tel: Tel: 0207 815 6340 Email: Email: zhaoza@lsbu.ac.ukzhaoza@lsbu.ac.uk

3. Textbook Alan V. Oppenheim, Ronald W. Schafer, Discrete-time Signal Processing, 2ed, Prentice Hall, ISBN: 0-13-083443-2

4. 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

5. Unit Calendar (Changes possible) Introduction to DSP1 Discrete-time signals 1-2 Discrete-time systems3-4 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 filters12 Revision 13 Examination 14-15

6. 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

7. 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.

8. 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).

9. Introduction to DSP 1.2 A General DSP System Anti-aliasing filter A/D DSP D/A Reconstruction filter Analog signal Analog signal Analog signal Analog signal Digital signal Digital signal

10. An Example

11. Introduction to DSP 1.3 Advantages: 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

12. Introduction to DSP 1.4 Disadvantages: Relatively low bandwidths Signal resolution is limited by the D/A and A/D converters.

13. 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

14. What is DSP Used For? …And much more!

15. Speech Recognition System Feature extraction speech Phoneme recognition Phoneme models Sentence recognition Word recognition Word pronunciation Semantic knowledge decision Syntactic knowledge Dialogue knowledge

16. Text-to-Speech Synthesis To be or not to be that is the question Text normalization expands abbreviations dates, times, money..etc ParsingPronunciation Prosody rules Tu bee awr nawt tu bee dhat iz dhe kwestchun semantic & syntactic ‘parts of speech’ analysis of text phonetic description of each word, dictionary with letter-to-sound rules as a back up Waveform generation Synthesized speech Apply word stress, duration and pitch Phonetic-to- acoustic transformation phonetic form Input text

17. Speech Coding – Vocoder 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:

18. JPEG Example Original JPEG (100:1)JPEG (4:1)