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. <br/

Predicting Individual Hearing Aid Preference from Self-Reported Listening Experiences in Daily Life

Objectives: The study compared the utility of two approaches for collecting real-world listening experiences to predict hearing-aid preference: a retrospective questionnaire (Speech, Spatial and Qualities of Hearing Scale, SSQ) and in-situ Ecological Momentary Assessment (EMA). The rationale being that each approach likely provides different and yet complementary information. Additionally, it was examined how self-reported listening activity and hearing-aid data-logging can augment EMAs for individualized and contextualized hearing outcome assessments. Design: Experienced hearing aid users (N = 40) with mild-to-moderate symmetrical sensorineural hearing loss completed the SSQ questionnaire and gave repeated EMAs for two wear periods of 2-weeks each with two different hearing-aid models that differed mainly in their noise reduction technology. The EMAs were linked to a self-reported listening activity and sound environment parameters (from hearing-aid data-logging) recorded at the time of EMA completion. Wear order was randomized by hearing-aid model. Linear mixed-effects models and Random Forest models with 5-fold cross validation were used to assess the statistical associations between listening experiences and end-of-trial preferences, and to evaluate how accurately EMAs predicted preference within individuals. Results: Only 6 of the 49 SSQ items significantly discriminated between responses made for the end-of-trial preferred versus non-preferred hearing-aid model. For the EMAs, questions related to perception of the sound from the hearing aids were all significantly associated with preference, and these associations were strongest in EMAs completed in sound environments with predominantly low SNR and listening activities related to television, people talking, non-specific listening, and music listening. Mean differences in listening experiences from SSQ and EMA correctly predicted preference in 71.8% and 72.5% of included participants, respectively. However, a prognostic classification of single EMAs into end-of-trial preference with a Random Forest model achieved a 95.2% accuracy when contextual information was included. Conclusions: SSQ and EMA predicted preference equally well when considering mean differences, however, EMAs had a high prognostic classifications accuracy due to the repeated-measures nature, which make them ideal for individualized hearing outcome investigations, especially when responses are combined with contextual information about the sound environment. <br/

Real-world benefits of DNN-based noise reduction (Christensen et al., 2024)

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. The first hearing aid (HA1) used traditional noise reduction; the second hearing aid (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: 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.Supplemental Material S1. Additional information on study design and sound data.Christensen, J. H., Whiston, H., Lough, M., Gil-Carvajal, J. C., Rumley, J., & Saunders, G. H. (2024). Evaluating real-world benefits of hearing aids with deep neural network–based noise reduction: An ecological momentary assessment study. American Journal of Audiology, 33(1), 242–253. https://doi.org/10.1044/2023_AJA-23-00149</p