Module CS5275 T

Duration

1 Semester

Turnus of offer

each summer semester

Credit points

4

Course of studies, specific fields and terms:

• Master Robotics and Autonomous Systems 2019, module part, Module part Current Issues Robotics and Automation
• Master Biophysics 2023, module part, advanced curriculum
• Master Computer Science 2019, module part, Module part
• Master MES 2020, module part, computer science / electrical engineering
• Master Entrepreneurship in Digital Technologies 2020, module part, Module part
• Master Biophysics 2019, module part, advanced curriculum
• Master IT-Security 2019, module part, Module part
• Master Entrepreneurship in Digital Technologies 2014, module part, Module part
• Master MES 2014, module part, computer science / electrical engineering
• Master Computer Science 2014, module part, Module part

Classes and lectures:

• Selected Topics of Signal Analysis and Enhancement (exercise, 1 SWS)
• Selected Topics of Signal Analysis and Enhancement (lecture, 2 SWS)

Workload:

• 45 hours in-classroom work
• 55 hours private studies
• 20 hours exam preparation

Contents of teaching:

• Introduction to statistical signal analysis
• Autocorrelation and spectral estimation
• Linear estimators
• Linear optimal filters
• Adaptive filters
• Multichannel signal processing, beamforming, and source separation
• Compressed sensing
• Basic concepts of multirate signal processing
• Nonlinear signal processing algorithms
• Application scenarios in auditory technology, enhancement, and restauration of one- and higher-dimensional signals, Sound-field measurement, noise reduction, deconvolution (listening-room compensation), inpainting

Qualification-goals/Competencies:

• Students are able to explain the basic elements of stochastic signal processing and optimum filtering.
• They are able to describe and apply linear estimation theory.
• Students are able to describe the concepts of adaptive signal processing.
• They are able to describe and apply the concepts of multichannel signal processing.
• They are able to describe the concept of compressed sensing.
• They are able to analyze and design multirate systems.
• Students are able to explain various applications of nonlinear and adaptive signal processing.
• They are able to create and implement linear optimum filters and nonlinear signal enhancement techniques on their own.

Grading through:

• exam type depends on main module

Responsible for this module:

• Siehe Hauptmodul

Teacher:

• Institute for Signal Processing
• Prof. Dr.-Ing. Markus Kallinger

Literature:

• A. Mertins : Signaltheorie: Grundlagen der Signalbeschreibung, Filterbänke, Wavelets, Zeit-Frequenz-Analyse, Parameter- und Signalschätzung Springer-Vieweg, 3. Auflage, 2013
• S. Haykin : Adaptive Filter Theory Prentice Hall, 1995

Language:

• offered only in German

Notes:

Last Updated:

08.03.2024