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.
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.
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
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
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.