ArticleTrends in hearing
Perceptual Effects of Adjusting Hearing-Aid Gain by Means of a Machine-Learning Approach Based on Individual User Preference.
Article in Trends in hearing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Scene-Specific Self-Tuning of Hearing Aids Enhances Perceived Speech Quality.American journal of audiology · 2025Article
- Preset Hearing Aid Program Selection in Low-Income Communities: A Longitudinal Study.Audiology research · 2025Article
- Artificial intelligence-enabled innovations in cochlear implant technology: Advancing auditory prosthetics for hearing restoration.Bioengineering & translational medicine · 2025Review
- A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids.Sensors (Basel, Switzerland) · 2024Review
- Evaluating and optimizing hearing-aid self-fitting methods using population coverage.Frontiers in audiology and otology · 2023Article
- Personalization of Hearing Aid Fitting Based on Adaptive Dynamic Range Optimization.Sensors (Basel, Switzerland) · 2022Article
- Personalizing over-the-counter hearing aids using pairwise comparisons.Smart health (Amsterdam, Netherlands) · 2022Article
- Feasibility study of personalized speed adaptation method based on mental state for teleoperated robots.Frontiers in neuroscience · 2022Article
- OPRA-RS: A Hearing-Aid Fitting Method Based on Automatic Speech Recognition and Random Search.Frontiers in neuroscience · 2022Article
- Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data.Journal of healthcare engineering · 2022Article
- The Collaboration between Hearing Aid Users and Artificial Intelligence to Optimize Sound.Seminars in hearing · 2021Review
- Optimizing Sound Quality Improves Hearing Aid Users' Moods and Listening-Related Fatigue.Trends in hearingArticle
- Application of Bayesian Active Learning to the Estimation of Auditory Filter Shapes Using the Notched-Noise Method.Trends in hearingArticle
- Preferred Strength of Noise Reduction for Normally Hearing and Hearing-Impaired Listeners.Trends in hearingArticle
- Article
- Computational Audiology: New Approaches to Advance Hearing Health Care in the Digital Age.Ear and hearingArticle
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigated a method to adjust hearing-aid gain by use of a machine-learning algorithm that estimates the optimal setting of gain parameters based on user preference indicated in an iterative paired-comparison procedure. Twenty hearing-impaired participants completed this procedure for 12 different sound scenarios. During the adjustment procedure, their task was to indicate a preference based on one of three sound attributes: Basic Audio Quality, Listening Comfort, or Speech Clarity. In a double-blind comparison of recordings of the processed scenarios, and using the same attributes as criteria, the adjusted gain settings were subsequently compared with two prescribed settings of the same hearing aid (with and without activation of an automatic sound-classification system). The results showed that the adjustment method provided a general improvement of Basic Audio Quality, an improvement of Listening Comfort in a traffic-noise scenario but not in three scenarios with speech babble, and no significant improvement of Speech Clarity. A large variation in gain adjustments was observed across participants, both among those who did benefit and among those who did not benefit from the adjustment. There was no clear connection between the gain adjustments and the perceived benefit, which indicates that the preferred gain settings for a given sound scenario and a given listening intention are highly individual and difficult to predict.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.