Course details

Digital Signal Processing (in English)

CZSa Acad. year 2023/2024 Winter semester 5 credits

Introduction to digital signal processing, sampling and quantization, Frequency analysis of digital signals, Principles of digital filters, Digital filter design, Practical implementation of digital filters. Processing in frequency domain, Sub-band signal processing, changing the sampling frequency, Wavelet analysis and synthesis, Random signals, State space representation, System identification, Wiener and Kalman filtering, Vector signal processing.

Guarantor

Course coordinator

Language of instruction

English

Completion

Examination (written)

Time span

  • 26 hrs lectures
  • 13 hrs exercises
  • 13 hrs projects

Assessment points

  • 51 pts final exam
  • 15 pts mid-term test
  • 14 pts numeric exercises
  • 20 pts projects

Department

Lecturer

Instructor

Learning objectives

To refresh basic knowledge of signals and systems and to make students familiar with more advanced topics linked to artificial intelligence, cyber-physical systems, speech and sound processing and other related domains. To provide students with sufficient mathematical background allowing to understand conference and journal papers dealing with signal processing topics, and allowing for own independent work in signal processing. To provide students with sufficient practical knowledge for implementing and integrating signal processing algorithms.

Study literature

Syllabus of lectures

  1. Introduction to digital signal processing, sampling and quantization.
  2. Frequency analysis of digital signals, DTFT, DFT and FFT. 
  3. Principles of digital filters. 
  4. Digital filter design. 
  5. Practical implementation of digital filters.
  6. Processing in frequency domain
  7. Sub-band signal processing, changing the sampling frequency.
  8. Wavelet analysis and synthesis.
  9. Random signals - correlation and power spectral density.
  10. State space representation. 
  11. System identification.
  12. Wiener and Kalman filtering.
  13. Vector signal processing

Syllabus of numerical exercises

Demonstration exercises (1h per week) immediately follow the lectures and demonstrate the taught techniques to the students based on real code, mostly in python and Matlab/Octave. All codes will be available to the students. Two homeworks (to be solved during the semester) are based on these exercises.

Syllabus - others, projects and individual work of students

The project is assigned in combination with another master course based on students specialization (for example in speech processing, or cyber-physical systems). It is solved in teams of up to 5 students, a report and short presentation are required. The data for projects will be provided, or acquired by the students. Examples of projects: 

  1. Simple signal processing for a microphone array  
  2. Estimation of transfer function of a mechanical system 
  3. Changing the properties of sound using time-frequency processing. 
  4. Sub-band audio coding.

Progress assessment

  • Solving and submitting solution of two home-works during the semester (7pts each, total 14pts) 
  • Half-semestral exam (15pts) 
  • Submission and presentation of project (20pts)
  • Semestral exam, 51pts, requirement of min. 17pts.


Schedule

DayTypeWeeksRoomStartEndCapacityLect.grpGroupsInfo
Mon exam 2024-01-08 A113 09:0010:50 řádná
Mon exam 2023-10-30 M104 M105 18:0018:50 Half semestral exam
Mon lecture 1., 2., 3., 4., 5., 6., 7., 8., 10., 11., 12., 13. of lectures M104 M105 18:0019:5041 1EIT 1MIT 2EIT 2MIT INTE NCPS NSPE xx Rohdin
Mon lecture 2023-11-13 M104 M105 18:0019:5041 1EIT 1MIT 2EIT 2MIT INTE NCPS NSPE xx Heřmanský, Rohdin
Mon exercise lectures N203 20:0020:5020 1EIT 1MIT 2EIT 2MIT INTE xx Rohdin
Tue exam 2024-01-23 A112 16:0017:50 2. termín
Wed exam 2024-01-17 A112 11:0012:50 1. termín

Course inclusion in study plans

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