Website
Module guide

Modul CS5275 T

Module part: Selected Topics of Signal Analysis and Enhancement (AMSAVa)

Duration:


1 Semester
Turnus of offer:


each summer semester
Credit points:


4
Course of studies, specific field and terms:
  • Master Robotics and Autonomous Systems 2019 (module part), Module part Current Issues Robotics and Automation, 1st and/or 2nd semester
  • Master Biophysics 2023 (module part), advanced curriculum, 2nd semester
  • Master Computer Science 2019 (module part), Module part, Arbitrary semester
  • Master MES 2020 (module part), computer science / electrical engineering, Arbitrary semester
  • Master Entrepreneurship in Digital Technologies 2020 (module part), Module part, Arbitrary semester
  • Master Biophysics 2019 (module part), advanced curriculum, 2nd semester
  • Master IT-Security 2019 (module part), Module part, 1st or 2nd semester
  • Master Entrepreneurship in Digital Technologies 2014 (module part), Module part, Arbitrary semester
  • Master MES 2014 (module part), computer science / electrical engineering, 1st or 2nd semester
  • Master Computer Science 2014 (module part), Module part, Arbitrary semester
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
Teachers:
  • 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:

(Part of modules CS4290, CS4510, CS5400, RO4290-KP04, CS5274-KP08)
(Is equal to CS5275)

For Details see main module.

Prerequisites for attending the module:
- None

Prerequisites for the exam:
- Successful completion of homework assignments during the semester (at least 50%).


Modul exam in Main module:
- CS5275-L1: Selected Topics of Signal Analysis and Enhancement, written or oral exam, 100% of modul grade

Letzte Änderung:
8.3.2024