Lecture "Adaptive Filters"

 

Basic Information
Lecturers: Gerhard Schmidt (lecture), Viktoriia Boichenko and Lukas Schirmer (exercise)
Room: Building F, room SR-IV
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Language: English
Target group: Students in electrical engineering and computer engineering
Prerequisites: Basics in system theory
Contents:

Students attending this lecture should learn the basics of adaptive filters. To achieve this, necessary algorithms will be derived and applied to problems arising in speech and audio processing. The algorithms comprise Wiener filtering, linear prediction, and adaptive schemes such as the NLMS algorithm, affine projection, and the RLS algorithm. For applications from speech and audio processing, we use noise and reverberation reduction, echo cancellation, and beamforming.

Topic overview:

  • Introduction and application examples
  • Signal properties and cost functions
  • Wiener filter and principle of orthogonality
  • Linear prediction
  • RLS algorithm
  • LMS algorithm and its normalized version
  • Affine projection algorithm
  • Control of adaptive filters
  • Efficient processing structures
  • Applications of linear prediction

 

News

If you’re reading this, the signal-to-news ratio is currently zero.

 

Schedule

The following schedule regarding lectures and exercises is preliminary and may be adapted during the semester.

Each event starts at 8:15 h and might use the whole slot (lecture and exercise) until 11:45 h.

Date Event
17.04.2026 Lecture: Introduction
08.05.2026 Lecture: Wiener filter
15.05.2026 Lecture: Linear prediction
22.05.2026 Exercise: Wiener Filter and linear prediction
29.05.2026 Lecture: Algorithms part I
05.06.2026 Lecture: Algorithms part II
12.06.2026 Lecture: Control
19.06.2026 Exercise: Algorithms and Control
26.06.2026 Lecture: Processing structures
03.07.2026 Exercise: Processing structures and questions
10.07.2026 Student talks and Lecture: Applications of linear prediction

 

Lecture Slides

Link Content
Slides of the lecture "Introduction"
(Introduction, boundary conditions of the lecture, applications)
Slides of the lecture "Wiener Filter"
(basics, principle of orthogonality, suppression of background noise)
Slides of the lecture "Linear Prediction"
(derivation of linear prediction, Levinson-Durbin recursion)
Slides of the lecture "Algorithms (Part 1 of 2)"
(RLS algorithm, LMS algorithm [part 1 of 2])
Slides of the lecture "Algorithms (Part 2 of 2)"
(LMS algorithm [part 2 of 2], affine projection algorithm)
Slides of the lecture "Control"
(basic aspect, pseudo-optimal control parameters)
Slides of the lecture "Processing Structures"
(polyphase filterbanks, prototype lowpass filter design)
Slides of the lecture "Applications of Linear Prediction"
(Improving the speed of convergence, filter design)

 

Extensions

Link Content
Extension for the lecture "Wiener Filter"
(derivation of the error surface)

 

Matlab Demos

Link Content
Matlab demo (GUI-based) for adaptive system identification
Matlab demo (GUI-based) for adaptive noise suppression
Matlab demo (GUI-based) for linear prediction
Matlab demo (GUI-based) for the NLMS algorithms
Matlab demo (GUI-based) for prediction-based filter design

 

Exercises

We provide additional videos and corresponding materials (e.g., questions and answers) for each topic below.

Exercises will be conducted according to the (preliminary) schedule above. Please register for the corresponding OLAT course to receive further information by mail (https://lms.uni-kiel.de/url/BusinessGroup/3276832778). Please bring a notebook for the exercises.

Video Content Material

Wiener filter:

  • summary
  • comprehension questions
  • python demo

Linear prediction:

  • summary
  • comprehension questions
  • signal visualization
  • python demo

Algorithms:

  • summary
  • explaining algorithms
  • comprehension questions
  • python demo

Control:

  • motivation/summary
  • comprehension questions
  • python demo

Processing structures:

  • summary
  • comprehension questions
  • python demo

 

Student Talks

As part of the lecture, each student will give a talk about a certain topic as a prerequisite to sitting the exam. The aim is both to give you the chance to work on an adaptive filter-related topic that interests you and to improve your presentational skills. The talks should take ten minutes, plus 2.5 minutes of discussion and 2.5 minutes of feedback.

Please contact us via This email address is being protected from spambots. You need JavaScript enabled to view it. with your topic suggestion until 27.06.2025. Below you can find the current schedule of the talks.

Handouts after a presentation are a valuable addition because they help the audience retain and review the key information. During a talk, listeners may miss important details or forget specific points. A well-prepared handout summarizes the main ideas, highlights essential data, and provides useful references for further reading. These references can be especially helpful if listeners want to explore the topic in more depth and continue learning on their own. It also allows participants to focus more on listening instead of taking extensive notes. Overall, handouts support understanding, reinforce learning, and make the presentation more effective and memorable.

For the reasons mentioned above, we have prepared a template for a handout. You can find that here. Please fill (and change) it with your presentation details and send it to us (same email address as mentioned above) before your talk.

Time Topic Presenter(s)
08:15 h Opening Gerhard Schmidt
08:30 h Example Topic Name Surname

 

Exams

If you do not have a date for the exam yet please register in the online booking system (once the exam dates have been set). You can find the booking system here.

 

Evaluations

The completed evaluations of the last years can be found here. Please note that this page contains the evaluations of all of our lectures. As a consequence, you must scroll down to get to the evaluations of this lecture.