Gesture recognition enables a natural extension of the way we currently interact with devices. Commercially available gesture recognition systems are usually pre-trained. We propose a method that allows users to define their own gestures using only a few training examples.

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We develop a fully probabilistic approach to pure-tone audiometry. By assuming a Gaussian process based response model for test tones, the hearing threshold estimation problem becomes one of Bayesian inference. This allows the use of information-theoretic criteria to select optimal test tones.

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We want to provide a hearing impaired patient with the best setting for her hearing aid device. By recording in-situ user feedback on device performance, we are able to better understand the specific hearing loss problem and preferences of the user. Using this knowledge, we can provide a better and personalized hearing experience.

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NIPS Workshop on Machine Learning for Audio Signal Processing

Frontiers in Computational Neuroscience 11:95, doi:10.3389/fncom.2017.00095

Network Neuroscience, The MIT Press Journals, Vol.0

More Publications

How do you solve a problem that is only vaguely described? We describe here an engineering approach that guides our research on solving vaguely defined problems such as hearing impairment.

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The primary mission of the BIASlab team is to develop in-situ trainable Bayesian Intelligent Agents for applications to wearable technology.

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Bert de Vries

Professor, TU Eindhoven

Tjalling Tjalkens

Associate Professor, TU Eindhoven

Ismail Senoz

PhD candidate, TU Eindhoven

Anouk van Diepen

PhD candidate, TU Eindhoven

Thijs van de Laar

PhD candidate, TU Eindhoven

Marco Cox

PhD candidate, TU Eindhoven

Ivan Bocharov

PhD candidate, TU Eindhoven

Quan (Eric) Nguyen

PhD Candidate, TU Eindhoven

Joris Kraak

Senior Software Engineer, GN Hearing

All members

In this project, you are challenged to develop novel machine learning technology for recognizing human motions.

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In this project, you are challenged to design an agent that learns to solve the cocktail party problem through on-the-spot interactions with a (human) listener.

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All vacancies

We gratefully acknowledge financial support from our sponsors:

GN Resound