Computational Intelligence, SS08
2 VO 442.070 + 1 RU 708.070 last updated:
General
Course Notes (Skriptum)
Online Tutorials
Introduction to Matlab
Neural Network Toolbox
OCR with ANNs
Adaptive Filters
VC dimension
Gaussian Statistics
PCA, ICA, Blind Source Separation
Hidden Markov Models
Mixtures of Gaussians
Automatic Speech Recognition
Practical Course Slides
Homework
Exams
Animated Algorithms
Interactive Tests
Key Definitions
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Introduction

Discrete-time (or digital) filters are ubiquitous in todays signal processing applications. Filters are used to achieve desired spectral characteristics of a signal, to reject unwanted signals, like noise or interferers, to reduce the bit rate in signal transmission, etc.

The notion of making filters adaptive, i.e., to alter parameters (coefficients) of a filter according to some algorithm, tackles the problems that we might not in advance know, e.g., the characteristics of the signal, or of the unwanted signal, or of a systems influence on the signal that we like to compensate. Adaptive filters can adjust to unknown environment, and even track signal or system characteristics varying over time.