Evaluating real-world benefits of hearing aids with Deep Neural Network-based noise reduction: An Ecological Momentary Assessment study

PURPOSE: Noise reduction technologies in hearing aids provide benefits under controlled conditions. However, differences in their real-life effectiveness are not established. We propose that a Deep Neural Network (DNN)-based noise reduction system trained on naturalistic sound environments will provide different real-life benefits compared to traditional systems.
METHOD: Real-life listening experiences collected with Ecological Momentary Assessments (EMAs) of participants who used two premium models of hearing aid (HA) are compared. HA1 used traditional noise reduction; HA2 used DNN-based noise reduction. Participants reported listening experiences several times a day while ambient sound pressure level (SPLs), signal-to-noise ratio (SNRs) and hearing-aid volume adjustments were recorded. 40 experienced hearing-aid users completed a total of 3614 EMAs and recorded 6812 hours of sound data across two 14-day wear periods.
RESULTS: Linear mixed-effects analysis document that participants’ assessments of ambient noisiness were positively associated with SPL and negatively associated with SNR but are not otherwise affected by hearing-aid model. Likewise, mean satisfaction with the two models did not differ. However, individual satisfaction ratings for HA1 were dependent on ambient SNR, which was not the case for HA2.
CONCLUSIONS: Hearing aids with DNN-based noise reduction resulted in consistent sound satisfaction regardless of the level of background noise compared to hearing aids implementing noise reduction based on traditional statistical models. While the two hearing-aid models also differed on other parameters (e.g., shape), these differences are unlikely to explain the difference in how background noise impacts sound satisfaction with the aids.