Advanced DSP methods

The course is not on the list Without time-table
Code Completion Credits Range Language
BE2M31DSP Z,ZK 6 2P+2C English

In order to register for the course BE2M31DSP, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.

Garant předmětu:
Department of Circuit Theory

The course follows the basic course in signal processing and introduces advanced methods of analysis and digital signal processing. Graduates will learn the methods of digital signals analysis and be able to practically use them. They learn to know the conditions of use of correlation, spectral and coherent analysis of random signals. They will became familiar with methods of signal decomposition and independent component analysis and the time-frequency transformations. Emphasis will be placed on an ability to interpret the results of signal analyses.


Basic knowledge of system theory and digital signal processing.

Syllabus of lectures:

1. Modeling and representation of linear systems in time-, correlation- and spectral-domain

2. Measurement of the delay using correlation and spectral analysis

3. Coherence, partial coherence and their use

4. Cepstral analysis and its use for signal deconvolution

5. Spectral and cepstral distances and their use

6. Methods of additive and convolution noise reduction and signal restoration

7. Methods of 1-D signal interpolation

8. Principal component analysis and its use for lossy compression of signals

9. Principles of methods of blind source separation

10. Principles of methods of blind signal deconvolution

11. Implementation of the discrete wavelet transform using filter bank, quadrature filters

12. Granger causality and Hilbert-Huang transform

13. Robust estimates of characteristics of random signals

14. Reserve

Syllabus of tutorials:

1. Representation of systems in time-, correlation- and spectral- domain

2. Methods of delay estimation and conditions of their proper use

3. Implementation of coherence function and its use

4. The use of cepstral analysis for signal deconvolution

5. Examples of spectral and cepstral distance use

6. Implementation of methods for additive and convolution noise reduction

7. Examples of interpolation of 1-D signals

8. Principal component analysis and its use for lossy compression of signals

9. Estimation of moments and cumulants of random signals

10. Examples of methods for blind separation and blind deconvolution

11. The use of discrete wavelet transform for noise reduction and signal analysis

12. Hilbert-Huang Transform use and properties

13. Robust estimates of random signal characteristics

14. Reserve, individual projects

Study Objective:

Students will acquire theoretical and practical experiences about advanced signal processing methods. They will deepen the ability of solving problems of digital signal processing in MATLAB within exercises and within their individual projects.

Study materials:

Saeed V. Vaseghi: Advanced Digital Signal Processing and Noise Reduction, Wiley,2009, ISBN: 978-0-470-75406-1

Monson Hayes: Statistical digital signal processing and modeling. Wiley, 1999, ISBN: 978-0-471-59431-4.

Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-04-23
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