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Digital Signal processing - B2B31CZS

Main course
Credits 4
Semesters Winter
Completion Assessment + Examination
Language of teaching Czech
Extent of teaching 2P+2C
Annotation
The subject gives overview about basic methods of digital signal processing and their applications (examples from speech and biological signal processing): disrete-time signals and systems, signal characteristics in time and frequency domain, Fourier transform, fast algorithms for DFT computation, introduction to digital filter design, digital filtering in time and frequency domain, decimation and interpolation and their usage in filter banks, basics of LPC analysis. Further details can be found at http://noel.feld.cvut.cz/vyu/ae2m99czs
Study targets
Students should acquire theoretical and practical experiences about basic DSP techniques and the most frequent applications. Simple implementations and simulations of basic DSP methods in MATLAB environment are solved in seminars of the subject. Extended level is possible to be managed within individual projects.
Course outlines
1. Introduction to DSP. Sampling theorem.
2. Basic characteristics of digital signals.
3. Autocorrelation and crosscorrelation functions
4. Fourier transform of discrete signal.
5. Properties of DFT, fast algorithms for DFT computation.
6. Spectral characteristics of stachastic and non-stationary signals.
7. Signal and system reprezentation in Z-domain 8. Digital Filtering I - FIR filters.
9. Digital filtering II - IIR filters.
10. Digital filtering in the frequency domain.
11. Basics of multi-band signal processing.
12. Basics of parametric methods of signal processing.
13. DSP applications in speech and biological signal processing. Signal compression.
14. Reserve.
Exercises outlines
1. Introduction to MATLAB and other tools 2. Computation of basic time-domain characterstics in MATLAB 3. Autocorrelation analysis and its applications 4. Discrete Fourier Transform (DFT) and its properties, interpolation, zero-padding 5. Spectral analysis of deterministic signals 6. Spectral analysis of stochastic and non-stationary signals 7. Discrete-time systems: basic properties, frequency repsonse 8. Design of digital FIR filters 9. Design of digital IIR filters 10. Digital filtering in frequency domain, implementation of signal segmentation 11. Parametric metods of DSP 12. Basics of multi-band signal processing 13. Consultations on semester works 14. Project presentation, credit
Literature
1] Oppenheim, A.V., Shafer, R.W.: Discrete-Time Signal Processing. Prentice-Hall, Inc., New Jersey, 1998 [2] Rabiner, L.R., Schafer, F.W.: Digital Processing of Speech Signals. Prentice-Hall, Inc., New York, 1978 [3] Makhoul, J.: Linear Prediction: A Tutorial Review. In Proceedings of the IEEE, Vol. 63, No. 4, April 1975 [4] The MathWorks: MATLAB User's and Reference Guides.
[5] The MathWorks: Signal Processing Toolbox - User's Guide.
(Any other book devoted to digital signal processing.)
Requirements
Bases of signal and system theory with necessary mathematical background are supposed as preliminary knowledge.

Digital Signal processing - B2M31CZS

Credits 5
Semesters Winter
Completion Assessment + Examination
Language of teaching Czech
Extent of teaching 2P+2C
Annotation
The subject gives overview about basic methods of digital signal processing and their applications (examples from speech and biological signal processing): disrete-time signals and systems, signal characteristics in time and frequency domain, Fourier transform, fast algorithms for DFT computation, introduction to digital filter design, digital filtering in time and frequency domain, decimation and interpolation and their usage in filter banks, basics of LPC analysis. Further details can be found at http://noel.feld.cvut.cz/vyu/ae2m99czs
Study targets
Students should acquire theoretical and practical experiences about basic DSP techniques and the most frequent applications. Simple implementations and simulations of basic DSP methods in MATLAB environment are solved in seminars of the subject. Extended level is possible to be managed within individual projects.
Course outlines
1. Introduction to DSP. Sampling theorem.
2. Basic characteristics of digital signals.
3. Autocorrelation and crosscorrelation functions
4. Fourier transform of discrete signal.
5. Properties of DFT, fast algorithms for DFT computation.
6. Spectral characteristics of stachastic and non-stationary signals.
7. Signal and system reprezentation in Z-domain 8. Digital Filtering I - FIR filters.
9. Digital filtering II - IIR filters.
10. Digital filtering in the frequency domain.
11. Basics of multi-band signal processing.
12. Basics of parametric methods of signal processing.
13. DSP applications in speech and biological signal processing. Signal compression.
14. Reserve.
Exercises outlines
1. Introduction to MATLAB and other tools 2. Computation of basic time-domain characterstics in MATLAB 3. Autocorrelation analysis and its applications 4. Discrete Fourier Transform (DFT) and its properties, interpolation, zero-padding 5. Spectral analysis of deterministic signals 6. Spectral analysis of stochastic and non-stationary signals 7. Discrete-time systems: basic properties, frequency repsonse 8. Design of digital FIR filters 9. Design of digital IIR filters 10. Digital filtering in frequency domain, implementation of signal segmentation 11. Parametric metods of DSP 12. Basics of multi-band signal processing 13. Consultations on semester works 14. Project presentation, credit
Literature
1] Oppenheim, A.V., Shafer, R.W.: Discrete-Time Signal Processing. Prentice-Hall, Inc., New Jersey, 1998 [2] Rabiner, L.R., Schafer, F.W.: Digital Processing of Speech Signals. Prentice-Hall, Inc., New York, 1978 [3] Makhoul, J.: Linear Prediction: A Tutorial Review. In Proceedings of the IEEE, Vol. 63, No. 4, April 1975 [4] The MathWorks: MATLAB User's and Reference Guides.
[5] The MathWorks: Signal Processing Toolbox - User's Guide.
(Any other book devoted to digital signal processing.)
Requirements
Bases of signal and system theory with necessary mathematical background are supposed as preliminary knowledge.