Machine learning models identify altered spontaneous brain connections in sound tolerance disorders
We evaluate a measure of individual preferences for noise vs. distortion toward more personalized fine-tuning of hearing aids
The validity of automated smartphone-facilitated in-situ audiometry
Target speaker-informed speech enhancement approaches can enhance speech perception in noisy multi-talker environments
Characterising frequency selectivity changes could provide further insights about the underlying cochlear deficits.
Authors: Amelie Hintermaier1; Iko Pieper1; Tamas Harczos1* 1audifon GmbH & Co. KG Background Hearing aid technology is constantly evolving, resulting in ever more sophisticated algorithms and increasingly powerful hardware. The…
WaveNet-based auditory model approximation is accurate, computationally efficient, and differentiable.
We used a multiplicity of measures to evaluate on-demand hearing-aid processing benefits for speech understanding and effort.
A “hilltop” approach for maximising loudness and minimising channel interactions in Cochlear Implant users
A neural network for diagnosing noise-induced hearing loss from the audioram achieved greater accuracy than existing methods
This study demonstrates reliable and consistent smartphone-based absolute threshold and loudness scaling tests in a home environment
Hearing loss and communication disability questionnaire (HEAR-COMMAND Tool) based on the ICF standard
We present a deep neural network that substitutes the cochlear implant sound coding strategy and performs speech denoising.