arXiv subjects

Hendrik Kayser

Publications and source records attributed to Hendrik Kayser.

3 recordsLinked to original sources

arXiv2026

The Virtual Hearing Clinic (VHC)- a modular online platform for hearing research and hearing health care

Objective: The aim is to introduce the concept of the Virtual Hearing Clinic (VHC), give an overview of the current status and exemplify its feasibility with data from a diagnostics module obtaining hearing thresholds. Design: The architecture of the VHC is described and an overview of functional modules that have been developed and tested in respective studies is given. As a functional example data from an experiment is presented. Hearing thresholds were obtained from 20 subjects with hearing loss with the VHC using the Graded Response Bracketing (GraBr) procedure and compared to reference thresholds obtained with a clinical audiometer. Results: Median VHC-based hearing thresholds over all frequencies did not differ significantly from the reference with values of 57.4 dB SPL (VHC) and 55.5 dB SPL (reference). The shape of the frequency-dependent thresholds was also found to be very similar for 250 Hz to 4 kHz. Results for 6 kHz showed larger differences. Conclusion: The VHC is suitable as a mobile hearing health application and to collect data for audiological research. By offering flexibility in time and location it can lower barriers for hearing health care and enable collecting large datasets that are needed for the advancement of data-driven audiology.

physics.med-ph↗

arXiv2024

Real-time multichannel deep speech enhancement in hearing aids: Comparing monaural and binaural processing in complex acoustic scenarios

Deep learning has the potential to enhance speech signals and increase their intelligibility for users of hearing aids. Deep models suited for real-world application should feature a low computational complexity and low processing delay of only a few milliseconds. In this paper, we explore deep speech enhancement that matches these requirements and contrast monaural and binaural processing algorithms in two complex acoustic scenes. Both algorithms are evaluated with objective metrics and in experiments with hearing-impaired listeners performing a speech-in-noise test. Results are compared to two traditional enhancement strategies, i.e., adaptive differential microphone processing and binaural beamforming. While in diffuse noise, all algorithms perform similarly, the binaural deep learning approach performs best in the presence of spatial interferers. Through a post-analysis, this can be attributed to improvements at low SNRs and to precise spatial filtering.

eess.AS↗

arXiv2022

Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA)

open Master Hearing Aid (openMHA) was developed and provided to the hearing aid research community as an open-source software platform with the aim to support sustainable and reproducible research towards improvement and new types of assistive hearing systems not limited by proprietary software. The software offers a flexible framework that allows the users to conduct hearing aid research using tools and a number of signal processing plugins provided with the software as well as the implementation of own methods. The openMHA software is independent of a specific hardware and supports Linux, macOS and Windows operating systems as well as 32-bit and 64-bit ARM-based architectures such as used in small portable integrated systems. www.openmha.org

eess.AS↗