Please use the online system to apply, there is no need to send your information via email.The application deadline is in January 2021, but earlier applications are encouraged and will be considered as soon as they are received.
For more information please contact Mr. Jonas Vanthornhout, tel.: +32 16 37 31 52, mail: firstname.lastname@example.org or Prof. dr. Tom Francart, tel.: +32 16 37 98 40, mail: email@example.com
You can apply for this job no later than December 21, 2020 via the online application tool
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This is a project in collaboration between the Department of Neurosciences (ExpORL) and the Department of Electrical Engineering (PSI), of the University of Leuven. Leuven is a lively small city in the center of Belgium located close to Brussels, with excellent quality of life, easy connections to European capitals, and home to one of Europe's oldest universities. The University of Leuven, founded in 1425, is a highly ranked research intensive university, and has been named the most innovative university in Europe by Reuters.
The research group of Experimental Otorhinolaryngology (ExpORL), Dept. Neurosciences aims to improve the hearing and communication of people with hearing impairment.The main research topics at ExpORL are:
* The neuroscience of speech perception
* Temporal neural processing in the auditory system
* Speech and music processing for cochlear implants
* Noise reduction for improved speech perception with hearing instruments in adverse and noisy listening environments
* Binaural hearing: signal processing schemes and evaluation procedures for bilateral acoustical and/or electrical hearing instruments
We focus on the chain of auditory modelling, signal processing, simulation, psychophysical tests, electro-physiological measurements, imaging, lab-implementation, and evaluation with normal hearing subjects and users of hearing aids and cochlear implants. Intensive research has led to fundamental knowledge about hearing and speech perception. Based on these studies, we have developed new signal processing strategies that are now used worldwide in the most recent cochlear implant systems and hearing aids. Furthermore, we have developed diagnostic and evaluation strategies that are widely clinically applied.
Our research group is strongly multidisciplinary, consisting of 35 researchers from a variety of backgrounds (engineering, audiology, medicine, ...)
The Center for Processing Speech and Images (PSI) applies cutting edge machine learning techniques to build better systems involving visual and auditory inputs, or both. Several spin-off companies have emerged from its activities.
The main research topics in speech and sound processing are:
• Automatic speech and speaker recognition
• Robust signal processing and speech enhancement
• Speech recognition of disordered voices and diagnostic speech tools
• Spoken language acquisition by machines
• Sound source segregation
• Semantic interpretation and retrieval of sound (e.g. find YouTube posts with laughter)
As during the previous decades computers became more powerful and the amount of sensorial data has increased, modeling and exploiting this large amount of data has become an important challenge and opportunity. As a research group combining the domains of computational science and machine learning, we aim to get more insight and solve practical applications in image and audio processing.
When" class="redactor-autoparser-object">https://www.esat.kuleuven.be/p... a person listens to sound, various parts of the auditory system are activated, including the brain. We can then measure the brain waves using EEG, decode them and draw conclusions about the auditory system.
We aim to develop a computational model of the auditory system, based on state-of-the art, deep-neural-network-based systems for automatic speech recognition. The model will be constructed by letting people listen to natural speech signals, and relating the recorded electroencephalogram (EEG) signal to the corresponding acoustic signals.
One of the outcomes of this project are brain wave decoders that can be used in novel neuro-steered hearing aids and potentially as a brain-computer interface. Furthermore, by modelling the auditory system, we could pinpoint the origin of hearing disorders which gives invaluable information for the diagnostics of the auditory system.
The work involves EEG signal processing, mostly using state-of-the art deep learning techniques. Besides deep learning, the work also encompasses classical signal processing, experimental design and data collection, and hypothesis-driven neuroscience research.
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The application deadline is in January 2021, but earlier applications are encouraged and will be considered as soon as they are received.