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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Digital signal processing

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Code Completion Credits Range Language
XP31DSP ZK 4 2+2s Czech
Lecturer:
Pavel Sovka (guarantor), Radoslav Bortel
Tutor:
Pavel Sovka (guarantor), Radoslav Bortel
Supervisor:
Department of Circuit Theory
Synopsis:

Relationships between transformations and their consequences, OLA and OLS signal reconstruction. Recursive filter realization, frequency sampling filters, recursive DFT-Goertzel algorithm. Short-time Fourier transform as a filterbank. Parametric methods: model types, analysis-synthesis,

linear prediction and estimation. Spectral and frequency analysis, noise and signal subspace, EVD and SVD. Cepstral analysis, problemas of liftering and signal deconvolution. Notes on adaptive filtering and blind source separation. Notes on wavelets and filter banks.

Requirements:
Syllabus of lectures:

1. Relationships between Fourier transforms FT, FS, DtFT, DtFS and DFT and consequences

2. Theory behind fast algorithms for DFT, Kronecker matrix multiplication

3. Lagrange interpolation in DSP, frequency sampling filters, Lynn filters, CIC filters

4. Relationship between short-time Fourier transform and filter banks, possibility of resampling

5. Homomorphic systems, theory of cepstral analysis, liftering, spectral envelope

6. Spectral and cepstral distances

7. Spectral factorization, minimum and non-minimum phase systems

8. Signal modelling using linear parametric methods

9. Signal analysis using linear parametric methods

10. MMSE-filters, notes on their performance

11. Karhunen-Loeve transform, singular value decomposition

12. Spectral and frequency estimation, principal components spectrum estimation

13. Blind separation - fixed-point algorithms

14. Granger causality in time and frequency domain

Syllabus of tutorials:

1. Relationships between Fourier transforms - sampling and windowing

2. Types of FFT algorithms, implementation issues

3. Implementing frequency sampling filters, Lynn filters, and CIC filters

4. Implementation of short-time Fourier transform

5. Use of real and complex cepstrum I

6. Use of real and complex cepstrum II

7. Computing spectral and cepstral distances

8. Examples of signal modelling algorithms

9. Effective implementation of parameter estimation algorithms

10. Examples of noise reduction using MMSE-filters

11. Application of Karhunen-Loeve transform

12. Spectral and frequency estimation algorithms

13. Examples of independent component analysis, Fast-ICA

14. Implementing Granger causality in time domain

Study Objective:
Study materials:

[1] Madisetti, V.K.: The Digital Signal Processing Handbook, CRC Press, 1998

[2] Lee, T. W.: Independent Component Analysis, Kluwer Academic Publishers, London, 1998

[3] Hayes, M. H.: Statistical Digital Signal Processing and Modeling, John Wiley&sons, New York, 1996

Note:
Time-table for winter semester 2018/2019:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
roomT2:A4-203b
Sovka P.
16:15–17:45
(lecture parallel1)
Dejvice
Učebna
Tue
Fri
Thu
Fri
Time-table for summer semester 2018/2019:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2019-04-18
For updated information see http://bilakniha.cvut.cz/en/predmet11844904.html