10 citations · 19 across the 4 of their papers we have counts for
6 papers
Deep learning-based denoising streamed from mobile phones improves speech-in-noise understanding for hearing aid users
Peter Udo Diehl, Hannes Zilly, Felix Sattler +11
The hearing loss of almost half a billion people is commonly treated with hearing aids. However, current hearing aids often do not work well in real-world noisy environments. We pr…
Restoring speech intelligibility for hearing aid users with deep learning
Peter Udo Diehl, Yosef Singer, Hannes Zilly +7
Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand…
A wake-sleep algorithm for recurrent, spiking neural networks
Johannes Thiele, Peter Diehl, Matthew Cook
We investigate a recently proposed model for cortical computation which performs relational inference. It consists of several interconnected, structurally equivalent populations of…
Learning and Inferring Relations in Cortical Networks
Peter U. Diehl, Matthew Cook
A pressing scientific challenge is to understand how brains work. Of particular interest is the neocortex,the part of the brain that is especially large in humans, capable of handl…
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware
Peter U. Diehl, Guido Zarrella, Andrew Cassidy +2
In recent years the field of neuromorphic low-power systems that consume orders of magnitude less power gained significant momentum. However, their wider use is still hindered by t…
TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth
Peter U. Diehl, Bruno U. Pedroni, Andrew Cassidy +3
We present an approach to constructing a neuromorphic device that responds to language input by producing neuron spikes in proportion to the strength of the appropriate positive or…