• Discrete-time signals: elementary signals, even/odd signals, harmonic exponential oscillations
• Fourier series of periodic discrete-time signals: Parseval theorem, symmetry relations
• Discrete-time systems: LTI, impulse response
• Fourier transform of discrete-time signals
• Sampling theorem
• Difference equations and z-transform
• FIR and IIR filter design
• DFT, FFT
• Cyclic convolution, overlap-add and overlap-save
• Multirate signal processing

After completing this course, students will be able to

• describe discrete-time signals and systems in the time and frequency domain using signal processing methods
• analyse and evaluate discrete-time systems with regard to stability, transient response, etc.
• design self-contained digital filters with specified properties
• realise digital filters in software in a computationally efficient manner
• implement more complex signal processing algorithms in Python in a computationally efficient manner

The students

• have acquired extensive skills in Python, which can also be used outside the realisation of signal processing algorithms,
• can design, implement and test a programme from a given task and evaluate, present and discuss the results obtained,
• can analyse more extensive tasks together in a group, break them down into subtasks and work on them in a solution-oriented manner.

Methodical realisation

• Lectures with predominantly blackboard use, occasional slide presentations
• Classroom exercises with exercise sheets and demonstrations on the computer
• Practical exercises with Python, in which students independently develop solutions and implement, test and analyse signal processing algorithms.

Doblinger, Gerhard: "Zeitdiskrete Signale und Systeme", An introduction to the basic methods of digital signal processing

• Course for Bachelor students
• ECTS: 6
• Language: German
• Semester: Summer term