Modulhandbuch

Communication and Media Engineering (CME)

Advanced Digital Signal Processing

Recommended prior knowledge

- Basics of continuous-time and discrete-time signals and systems (impulse response, step response, frequency response)
- Fourier Series, Fourier Transformation, Laplace Transformation, z-Transformation
- Lecture "Digital Signals and Systems" (E+I403)

Teaching Methods Vorlesung/Labor
Learning objectives / competencies

The students acquire:

- Profound knowledge of digital signal processing systems
- Ability to implement modern signal processing concepts

Duration 2
SWS 5.0
Effort
Classes 75 h
Self-study / group work: 105 h
Workload 180 h
ECTS 6.0
Requirements for awarding credit points

Advanced Digit. Signal Processing: written exam K90
DSP Lab must be passed.

Credits and Grades

6 CP / Grades 1.0 / 1.3 / 1.7 / 2.0 / 2.3 / 2.7 / 3.0 / 3.3 / 3.7 / 4.0 / 4.3 / 4.7 / 5.0

 

Responsible Person

Prof. Dr.-Ing. Werner Reich

Recommended Semester 2/3
Frequency jedes 2. Semester
Usability

Master-Studiengang CME

Lectures

Digital Signal Processing Lab Work

Type Labor
Nr. EMI415
SWS 1.0
Lecture Content

Experiment 1: A-to-D and D-to-A-Conversion
- Aliasing Effect
- Mirror Components
- (sin x)/x-Distortion
- Quantization Effects: Estimation of Signal-to-Noise-Ratio
- Nonlinearity of D-to-A-Converter
- Subjective Listening Tests

Experiment 2: Finite Impulse Response (FIR-) Filters
- Filter Design Using the Fourier Approximation
- Modification by Using Window Functions
- Optimum Design (Parks-McClellan-Algorithm)
- Finite Precision Effects
- Design of Hilbert Filters (Wideband Phase Shifters)

Experiment 3: Fast Fourier Transformation
- Speed Measurements
- Spectral Analysis, Windows to reduce Leakage Effects
- Comparison of direct and fast Implementation of Correlation
- Comparison of direct and fast Convolution

 

Literature

"User's Guides" for the Experiments

 

Advanced Digit. Signal Proc.

Type Vorlesung
Nr. EMI414
SWS 4.0
Lecture Content
  • Transform Analysis of Linear Time-Invariant Systems: Frequency Response Components, All-Pass Filters, Minimum-Phase Systems.
  • IR Filter Design: Approximation of Differential Equation, Impulse and Step Invariance Design, Bilinear Transformation.
  • IIR Filter Structures: Noncanonical and Canonical Direct Form, Transposed Direct Form, Parallel Form, Cascade Form. Finite Precision Numerical Effects.
  • FIR Filter Design Techniques: Fourier Approximation, Windowing, Optimum Equiripple Approximation.
  • Discrete Fourier Transform (DFT): Linear and Circular Convolution, Fast Fourier Transform (FFT) Algorithms.
  • Multirate Processing: Downsampling, Decimation Filter, Upsampling, Interpolation Filter.
  • Adaptive Signal Processing: Configuration in different Applications, Optimum Filter, Least-Mean-Squares Algorithm.
Literature

Oppenheim, Alan V.; Schafer, Ronald W.: Discrete-Time Signal Processing. Pearson, 2013.