CTU FEE Moodle
Digital Signal Processing
B241 - Winter 24/25
This is a grouped Moodle course. It consists of several separate courses that share learning materials, assignments, tests etc. Below you can see information about the individual courses that make up this Moodle course.
Digital Signal Processing - AE2M99CZS
Main course
Credits | 5 |
Semesters | Winter |
Completion | Assessment + Examination |
Language of teaching | English |
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.
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
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. 3rd edition. Prentice-Hall, 2009
[2] Vaseghi S.V..: Advanced Digital Signal Processing and Noise Reduction. 4th edition. Wiley, 2008
[3] The MathWorks: MATLAB User's and Reference Guides.
(Any other book devoted to digital signal processing.)
[2] Vaseghi S.V..: Advanced Digital Signal Processing and Noise Reduction. 4th edition. Wiley, 2008
[3] The MathWorks: MATLAB User's and Reference Guides.
(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 - BE5B31CZS
Credits | 5 |
Semesters | Winter |
Completion | Assessment + Examination |
Language of teaching | English |
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/be5b31czs .
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.
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
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. 3rd edition. Prentice-Hall, 2009
[2] Vaseghi S.V..: Advanced Digital Signal Processing and Noise Reduction. 4th edition. Wiley, 2008
[3] The MathWorks: MATLAB User's and Reference Guides.
(Any other book devoted to digital signal processing.)
[2] Vaseghi S.V..: Advanced Digital Signal Processing and Noise Reduction. 4th edition. Wiley, 2008
[3] The MathWorks: MATLAB User's and Reference Guides.
(Any other book devoted to digital signal processing.)
Requirements
Bases of signal and system theory with necessary mathematical background are supposed as preliminary knowledge.