TjallingTjalkens is Associate Professor in the Signal Processing Systems group at TU/e. His research interest is information theory, especially universal and non-universal source coding theory and machine learning. He is known for developing the universal Context Tree Weighting data compression algorithm. Tjalkens has done research projects on noiseless source coding, the universal variable-to-fixed length modified Lawrence algorithm, the redundancy rate of the well-known Lempel-Ziv universal algorithm and channel coding. In 2014, Tjalkens received a grant from the Dutch technology foundation STW in the High Tech Systems and Materials program. He is using the funds to develop algorithms for a personalized hearing system. He occasionally works as a consultant for KPN on general data compression issues. Tjalkens authored and co-authored over 50 papers and holds two patents on data compression systems.
TjallingTjalkens holds an MSc degree in Electrical Engineering from Eindhoven University of Technology (TU/e) and received his PhD from the same university in 1987. During his PhD, he worked as research assistant at TU/e. In 1986, he became a staff member here. He worked as visiting scientist at the Technical University of Lund (Sweden) in 1989 and in 1999. From 1996 to 2001, Tjalkens was a board member of WIC and from 1999 to 2003, he was board member of NERG. From 2011 to 2014 he participated in the European Union project EnLight.
Acoustic scene classification from few examples26th European Signal Processing Conference, EUSIPCO 2018 (2018)
Using feature-based models with complexity penalization for selecting featuresJournal of Signal Processing Systems (2018)
Smart office lighting control using occupancy sensors14th IEEE International Conference on Networking, Sensing and Control (2017)
K-shot learning of acoustic contextNIPS Workshop on Machine Learning for Audio Signal Processing (2017)
Probabilistic inference-based reinforcement learning(2017)
- Rock your baby
- Applications of information theory
- Adaptive information processing
- Information theory
No ancillary activities