CTU FEE Moodle
Advanced DSP methods
B261 - Winter 26/27
Advanced DSP methods - B0M31DSP
| Credits | 6 |
| Semesters | Winter |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 2P+2C |
Annotation
The course introduces advanced methods of analysis and processing of digital signals such as correlation, spectral, coherence or cepstral analysis, as well as methods of decomposition into principal and independent components, methods for determining the relationship between random signals and basic classification techniques used in signal analysis. Attention is paid to practical applications of the mentioned techniques, e.g. for noise suppression or compression.
Study targets
Students will learn to use the above-mentioned advanced signal analysis techniques, interpret the results obtained, and practically use basic classification techniques.
Course outlines
1. LPC analysis: calculation of AR model parameters, LPC spectrum
2. General signal modeling (AR, MA, ARMA)
3. Delay measurement using correlation and spectral analysis
4. Coherence function, magnitude square coherence (MSC) and its application
5. Cepstral analysis and its application
6. Spectral and cepstral distance and their application
7. Reduction of additive and convolutional noise in the spectral and cepstral domains
8. Discrete cosine transform
9. Principal component analysis (PCA) as a basis for lossy signal compression
10. Basics of classification (k-means, GMM, SVM)
11. Use of neural networks in signal processing
12. Implementation of discrete wavelet transform by filter bank, quadrature filters
13. Principles of blind separation and deconvolution methods of signals
14. Reserve
2. General signal modeling (AR, MA, ARMA)
3. Delay measurement using correlation and spectral analysis
4. Coherence function, magnitude square coherence (MSC) and its application
5. Cepstral analysis and its application
6. Spectral and cepstral distance and their application
7. Reduction of additive and convolutional noise in the spectral and cepstral domains
8. Discrete cosine transform
9. Principal component analysis (PCA) as a basis for lossy signal compression
10. Basics of classification (k-means, GMM, SVM)
11. Use of neural networks in signal processing
12. Implementation of discrete wavelet transform by filter bank, quadrature filters
13. Principles of blind separation and deconvolution methods of signals
14. Reserve
Exercises outlines
1. LPC analysis, LPC spectrum
2. Signal modeling (AR, MA models of the 1st and 2nd order)
3. Delay measurement based on cross-power spectral density
4. Properties and applications of the coherence function
5. Real and complex cepstrum - definition and basic properties
6. Cepstral distance
7. Suppression of additive noise in the frequency domain
8. Calculation and use of the discrete cosine transform
9. Principal component analysis and KLT transform
10. Classification based on k-means
11. Classification based on GMM
12. Noise suppression based on ANN
13. Wavelet transform, implementation by a filter bank, noise suppression based on WT
14. Reserve
2. Signal modeling (AR, MA models of the 1st and 2nd order)
3. Delay measurement based on cross-power spectral density
4. Properties and applications of the coherence function
5. Real and complex cepstrum - definition and basic properties
6. Cepstral distance
7. Suppression of additive noise in the frequency domain
8. Calculation and use of the discrete cosine transform
9. Principal component analysis and KLT transform
10. Classification based on k-means
11. Classification based on GMM
12. Noise suppression based on ANN
13. Wavelet transform, implementation by a filter bank, noise suppression based on WT
14. Reserve
Literature
[1] Oppenheim, A. V., Schaffer, R. W. : Discrete-Time Signal Processing. Prentice-Hall, 3rd edition, 2009.
[2] S. V. Vaseghi: Advanced Digital Signal Processing and Noise Reduction, Wiley, 2009.
[3] M. Hayes: Statistical digital signal processing and modeling. Wiley, 1999.
[2] S. V. Vaseghi: Advanced Digital Signal Processing and Noise Reduction, Wiley, 2009.
[3] M. Hayes: Statistical digital signal processing and modeling. Wiley, 1999.
Requirements
Knowledge of basic techniques of digital signal processing, digital filtering as well as mathematical apparatus for describing continuous and discrete signals and systems is assumed.