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Title: AIDAR (Adressage et Indexation de Documents Multimédias Assistés par des Techniques de Reconnaissance Vocale)
Partner: Voice-Insight S.A., Titan asbl
Funding: Région de Bruxelles-Capitale
Project number:
Duration: 2004-2006

Project overview:

Nowadays we assist to an increasing need for an automatic and unifed method for indexing audio archives of audiovisual companies. Currently, most of the work is accomplished by a huge workforce of human experts. This project aims to develop an automatic architecture featuring the following functionalities:
  • Segmentation audio
  • Classification of radio news by topic categories (economy, war, politic, sport, ...)
  • Automatic indexing and retrieval methods for querying the database of news and programs
The Titan asbl (in association with RTBF) is in charge of providing radiophonic data news and shows. The Voice-Insight company will provide know-how on voice recognition technology. The MLG is in charge of developing and testing machine learning techniques for automatic indexation.

Machine Learning Group contribution:

Main contribution of the MLG will be for topic classification. After using a full speech recognizer, we will get the full text of the news. With our "know-how" of machine learning techniques, we are in charge to automatically detect the topic of radiophonic news. Method such as Lazy learning (k-nearest neighbour), SVM (Support Vector Machine), and LLSF (Linear Least Square Fit) are widely used in text classification. We are going to investigate most of these techniques to find the best ones for the architeture of AIDAR.

MLG researcher involved:

Benjamin Tshibasu-Kabeya (Machine Learning Group - Computer Science Department - Université Libre de Bruxelles - Supervisor : Prof. Gianluca Bontempi).