Recommended prerequisite for participation in the module

The module builds on basic knowledge of signal processing concepts and algorithms (digital filtering and discrete transfer functions, IIR and FIR filters)

Content, progress and pedagogy of the module

This course provides an overview of advanced digital signal processing techniques and their implementation for different applications.

This course extends the basic signal processing concepts to more complicated problems and advanced techniques. Theory and practical issues in implementations are covered.

Learning objectives

Knowledge

Students must have knowledge about:

• Parametric spectral analysis (e.g. LPC, Lattice)
• Adaptive signal processing (e.g. LMS, NLMS)
• Model-based signal processing (e.g. State-Space, Kalman)
• Multi-dimensional signal processing (e.g. extension of discrete Fourier transform and convolution theorem)
• Algorithms for genetic and statistical signal processing
• Implementation of signal processing techniques for different applications (e.g. in speech recognition, sensor and control systems, communication systems)

Skills

• Must be able to implement the knowledge and techniques given in the course to solve practical problems.
• Must be able to validate and evaluate the performance of the methods.

Competences

• Must have competencies in problem formulation for different applications and identifying appropriate signal processing techniques to the problem.
• Must have competencies in applying signal processing methods in the applications.

Type of instruction

As described in §17

Exam

Exams

Name of exam	Advanced Signal Processing
Type of exam	Written or oral exam
ECTS	5
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures