Evaluating Real-World Benefits of Hearing Aids With Deep Neural Network-Based Noise Reduction: An Ecological Momentary Assessment Study

Abstract

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 are compared. One hearing aid model (HA1) used traditional noise reduction; the other hearing aid model (HA2) used DNN-based noise reduction. Participants reported listening experiences several times a day while ambient SPL, SNR, and hearing aid volume adjustments were recorded. Forty experienced hearing aid users completed a total of 3,614 EMAs and recorded 6,812 hr 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: 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.