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OVERVIEW

The focus of the neural networks group is to investigate aspects of training and optimization of neural networks, and to apply neural networks to solve real-world problems. The activities of this focus area are mainly centered around architecture selection, active learning, and the development of new an efficient training algorithms. Some work is done on self-organizing maps.

Current applications are directed towards data mining, spam detection, user authentication, fraud detection, gesture recognition, and trading on financial markets. Applications of self-organization maps to exploratory data analysis, data mining, and species identification are done.

ACTIVE MEMBERS

List the current members actively doing research in this focus area. [ Show ]

ALUMNI MEMBERS

S van der Stockt

M.Sc Completed in 2008

E Dean

M.Sc Started in 2002

J Pun

M.Sc Completed

E Clements

Hons-B.Sc Completed in 2003

U Paquet

M.Sc Completed in 2003

R van den Hoven

Hons-B.Sc Completed in 2003

A Ismail

PhD Started in 2005
M.Sc Completed in 2001

A Adejumo

M.Sc Completed in 1999

GROUP PUBLICATIONS

List publications of this research focus area. [ Show ]

MEMBER PROFILE



 Name:

 Eric Clements

Portrait photo

 E-mail:

 e.clem@iafrica.com

 Group(s):

 Neural Networks

 

 Degree specific information: M.Sc

 Title:

 Music Recognition with Computational Intelligence

 Abstract:

In the field of Audio Signal processing, extensive research has been exercised in the field of voice recognition. An area that seems to have had less attention is research focussing on music recognition. This thesis will focus on feature extraction and classification of audio samples for the purpose of music recognition. The first phase of the research is to define features which represent audio samples (specifically music samples) in a mathematically structure that can be processed by computational algorithms such as neural networks and clustering applications. The second phase of the research will include experimentation with different (available) computational intelligence algorithms to determine the best suited algorithm for classifying the music recognition features defined in the first phase.

 Supervisor / Co-Supervisor:

 AP Engelbrecht
 N Franken

 Thesis:

 Not available for download yet.

 

 Degree specific information: Hons-B.Sc

 Title:

 Real-time Genre Classification of Music using Self-Organizing Maps

 Abstract:

Until recently, electronically stored music had to be classified manually by means of tagging approaches where a human has to select the genre of a song. This approach is repetitive, inaccurate and cumbersome. The need for classification of music is still large and therefore this research paper suggests a computerized system that can be used to automatically classify music genres. Categorizing digital audio is not the only application of this system. The system can also be used in music visualization systems, similar to the visualization plug-ins in popular mp3 audio players. A visualization program using the Real-time Genre Classification System will have the added feature of changing colour, form and other features based on the genre of the music.

 Supervisor / Co-Supervisor:

 AP Engelbrecht
 N Franken

 Thesis:

 Not available for download yet.




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