Cognitive Signal Processing Group

Research Areas

Robust systems and algorithms for automatic speech and pattern recognition:

Our group's activities are focused on achieving optimal information integration in  machine perception. We are working on a tighter coupling of machine learning and statistical signal processing, aiming at a closer interaction of graph algorithms, probabilistic modelling and deep learning.

On the applications side, we are using audiovisual input for highly robust speech recognition, reliable and interpretable human-machine interaction, and for video-based speech quality enhancement.

Signal processing and machine learning are also used as tools for technical diagnostics and automatic fault monitoring.

Teaching Experience: Lectures, Student Projects, Programming Courses (Matlab, Simulink) of DSP, Speech Recognition, Microprocessor Architecture

Specialties: Speech Processing, Acoustic and Audiovisual Speech Recognition, Machine Learning, Deep Learning, Signal Processing, Microprocessor Systems, Control System Design

Pattern Recognition for Communication and Technical Diagnostics

While originally developed for robust speech recognition, the idea of using time-variant reliability information is also valuable for pattern analysis and recognition in general.

In collaboration with institutional and industrial partners, we are currently working on extending the idea of uncertainty-of-observation techniques both to more reliable data transmission and to the fault diagnosis of complex technical systems.




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