Algorithms of Signal Processing

The course is a substitute for:

Algorithms of Signal Processing (X31ASI)

Lecturer:

Tutor:

Supervisor:

Department of Circuit Theory

Synopsis:

The aim is to give the principles of algorithms used in modern digital systems. They are studied: filtration in time and frequency domain, structures of systems, parametric methods (AR, MA, ARMA), linear prediction (LPC), LPC characteristics, DFT and LPC based spectral analysis. The exercises are focused on solution of project in a chosen topic - usually algorithms implementation and simulation in MATLAB. Required knowledge of digital signals and systems theory.

Requirements:

The elaboration and representation of one selected problem is the condition for the credit.

Syllabus of lectures:

1. Short-term and long-term signal characteristics in time and frequency domain

2. Overlap-add (OLA) method of signal synthesis

3. Filtration in time and frequency domain, lattice structure

4. Parametric methods (AR, MA, ARMA), linear prediction (LPC)

5. LPC characteristics, DFT and LPC based spectral analysis

6. Application of spectral analysis for speech and industrial processing

7. Cepstral analysis, echo detection and suppression

8. Application of cepstral analysis for signal detection in noise

9. Robust parameterization of signals in speech processing and prediction

10. Correlation analysis, delay compensation, noise cancellation

11. Coherence analysis, signal detection and separation

12. Application of adaptive filtering in spectral analysis and noise cancellation

13. Time-frequency analysis, nonlinear frequency transformations

14. Orthogonal transformations, data compression

Syllabus of tutorials:

1. Signal generation and signal databases

2. Short-term and long-term spectrum, visualization

3. OLA implementation

4. Filtration in frequency domain, spectral subtraction

5. Objective criteria for signal quality, SNR estimation

6. Implementation of parametric methods in MATLAB

7. Implementation of DFT and AR spectral analysis

8. Implementation of cepstral analysis, signal reconstruction

9. Spectral and cepstral distances, signal detection in noise

10. Cepstral liftering and applications

11. Implementation of adaptive filters in MATLAB

12. Delay compensation in speech processing

13. Transfer function measurement, coherence analysis

14. Project presentation, credit

Study Objective:

Study materials:

1. Bendat, J., Piersol, A.: Random Data: Analysis Measurement Procedures, John Wiley & Sons, New York, 1971

2. Openheim, A.V., Shafer, R.W.: Discrete-Time Signal Processing. Prentice-Hall, Inc., New Jersey, 1990

3. Rabiner, L.R., Schafer, F.W.: Digital Processing of Speech Signals. Prentice-Hall, Inc., New York, 1978

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: