Evaluating the benefit of hearing aids with motion-based beamformer adaptation in a real-world setup

Abstract

Objective: Conventional directional hearing aid microphone technology may obstruct listening intentions when the talker and listener walk side by side. The purpose of the current study was to evaluate hearing aids that use a motion sensor to address listening needs during walking. Design: Each participant completed two walks in randomised order, one walk with each of two hearing aid programs: (1) conventional beamformer adaptation that activated an adaptive, multiband beamformer in loud environments and (2) motion-based beamformer adaptation that activated a pinna-mimicking microphone setting when walking was detected. Participants walked along a pre-defined track and completed tasks assessing speech understanding and environmental awareness. Study Sample: Participants were 22 older adults with moderate-to-severe hearing loss and experience using hearing aids. Results: More participants preferred the motion-based than conventional beamformer adaptation for speech understanding, environmental awareness, overall listening, and sound quality (p < 0.05). Measures of speech understanding (p < 0.01) and localisation of sound stimuli (p < 0.05) were significantly better with motion-based than conventional beamformer adaptation. Conclusions: The results suggest that hearing aid users can benefit from beamforming that uses motion sensor input to adapt the signal processing according to the user's activity. The real-world setup of this study had limitations.

Beamforming and Single-Microphone Noise Reduction: Effects on Signal-to-Noise Ratio and Speech Recognition of Bimodal Cochlear Implant Users

“…It operates independently on the CI and HA. The algorithm is an adaptive multiband beamformer (Voss et al, 2021) and attempts to maximize the attenuation of sounds originating from the back hemisphere by estimating the region with the lowest signal-to-noise ratio (SNR) in the auditory scene. The region of maximal attenuation (the null direction) is steered toward the noise-dominated region, potentially further increasing the SNR by noise suppression (Buechner et al, 2014;Hehrmann et al, 2012).…”

Human Head Yaw Estimation Based on Two 3-Axis Accelerometers

“…Those systems can be used to steer audio processing algorithms aiming at enhancing speech intelligibility and/or spatial awareness [4,5]. Voss et al [6] showed significant improvement in speech understanding, sound localization as well as preference in hearing-impaired listeners with such steering approaches. Being able to track the head motions of a user is of high interest since this would allow controlling the beam of a directional microphone system for example.…”