Jörn Anemüller's Homepage


April 2006
During the summer term I am acting as substitute professor for signal processing, since Alfred Mertins moved to Lübeck University.
Teaching related info is located here.

November 2006
Course info for the seminar "Elemente der Sprach- und Mustererkennung" is located here.

July 2006
Paper on convolution modeling of fMRI using complex ICA published in Neurocomputing. See the Publications section.

March 2006
Paper on multidimensional ICA model published at the ICA 2006 conference. See the Publications section.

January 2006
DIRAC (Detection and Identification of Rare Audio-Visual Cues) started, an Integrated Project funded by EC.

September 2005
Paper on the new OLLO (Oldenburg Logatome) speech corpus published, see the Publications section. OLLO provides speech with natural variabilities and is used in the Medical Physics section to compare speech recognition by humans and machines. The corpus can be downloaded from http://sirius.physik.uni-oldenburg.de.

April 2005

Seminar announcement for Oldenburg University:

Veranstaltung 5.04.430: Independent Component Analysis

Zeit und Ort: Mi 16 - 18, W02 1-156

BEGINN: 20.4.2005 (!)

Veranstalter: Jörn Anemüller


Independent component analysis (ICA) is a relatively novel technique for data analysis that has proven useful in many situations where traditionally approaches like principal component analysis (PCA, aka Karhunen-Loeve transform) have been used. In a nutshell, ICA assumes that measured data is generated by several underlying 'source' processes which are mutually independent, e.g., because they constitue physically separated systems. While the sources are independent, the recorded signals are mutually dependent mixes of the sources, where the mixing may, e.g., be due to signal propagation in a physical medium such as air. The goal of ICA is to reconstruct the sources from knowledge of the recorded signal, only. Knowledge about the mixing system is not assumed to be know a-priori, therefore ICA methods are often termed as blind source separation (BSS). The description of measured data in terms of several independent signals may be regarded as the 'most informative'description of the data, which makes ICA a suitable analysis tool for many applications.
One application of ICA is acoustic source separation where individual speaker signals are separated from mixed signals recorded with several microphones. Further applications cover such diverse areas as data mining, feature extraction, description of natural signals, stock market analysis, etc.
The seminar will start out with introducing the basic ICA assumptions and models. Next, convolutive ICA methods will be introduced which are used in solving the acoustic source separation problem. Finally, more general topics will be covered such as nonlinear ICA and ICA for feature extraction, and applications such as biomedical data analysis from EEG and fMRI, astronomical data analysis, and financial data analysis.
Along the way, students will be introduced to several general techniques such as numerical optimization, neural networks, information theory and statistical signal processing.

Grundstudium, interest in real-world data analysis (physical and non-physical systems)

Hyvärinen, Karhunen, Oja: Independent Component Analysis, Wiley, New York, 2001

Online introductions:
http://www.cis.hut.fi/projects/ica/book/intro.pdf http://www.cs.helsinki.fi/u/ahyvarin/whatisica.shtml http://www.cis.hut.fi/projects/ica/icademo/ http://www.cis.hut.fi/projects/ica/cocktail/cocktail_en.cgi

October 2004
Starting October, I will be back in Germany at the Dept. of Physics at Oldenburg University.

Septermber 2004
Two papers to be presented at the ICA 2004 conference in Granada, Spain, are now online in the Publications section.