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Signal and Information Processing: Modeling, Filtering, Learning
Last Updated: 2026-02-05 14:55:15
Content
The course is an introduction to some basic topics in linear, nonlinear, and adaptive signal processing, with application examples from acoustics, communications, and biomedical signal processing. Topics: linear filters and filter banks, FFT and fast convolution, basics of wavelets, Hilbert spaces; adaptive filters, LMS and RLS, neural networks, support vector machines; hidden Markov models, Kalman filtering and smoothing, particle filters, factor graphs; application examples from acoustics, communications, and biomedical signal processing.
General Information
- Language
- English
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 30 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Signal and Information Processing: Modeling, Filtering, Learning |
|
4 h weekly |