Non-linear signal processing and pattern recognition

NOTE : For students that follow the course, reading material and news will be put on CAMPUSNET. Contact the responsible lecturer (Peter Ahrendt - (pah if there are problems with access.


Practical info

The course starts Thursday 11th of April 2013 - 12:15 to 16:00

Lectures will be on Mondays 14:15 to 16:00 and Thursdays 12:15 to 16:00

Textbook - "Pattern Recognition and Machine Learning" by Christopher Bishop

Responsible lecturer - Peter Ahrendt, Aarhus School of Engineering, AU (pah



The following text gives a short description of the course - for more info, see course catalogue

Course background

The aim of the course is to give participants insight into methods for Nonlinear Signal Processing and Pattern Recognition of real world signals such as music, images/video or physiological signal from the human body. Participants will gain experience with nonlinear models of such signals, where techniques from estimation­/detection theory and machine learning will be used for identification of suitable models and further analysis and decision making based on the signals. Participants will obtain practical experience, through work on case project.

Course contents

The course will present nonlinear methods for analyzing and decision making based on real world signals. Techniques from estimation/detection theory and machine learning will be presented and applied on specific cases. The student should choose a main topic which will form the basis of the mandatory final report that is part of the examination. Possible topics could be :  

1.     Gesture Recognition (accelerometer signals).

2.     Music Genre Classification (sound signals).

3.     Face Recognition (image/video signal).

4.     Human Computer Interface based on brain signals (physiological signal from the human body).