Efficient Personalization of Amplification in Hearing Aids via Multi-Band Bayesian Machine Learning

• Published in: IEEE access Vol. 12; pp. 112116 - 112123
• Main Authors: Ni, Aoxin, Lobarinas, Edward, Kehtarnavaz, Nasser
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amplification; Auditory system; Bayes methods; Bayesian analysis; Bayesian learning; Customization; Frequencies; Gain; Hearing aids; Indexes; Machine learning; personalization of hearing aids amplification; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3441762

Personalization of the amplification function of hearing aids has been shown to be of benefit to hearing aid users in previous studies. Several machine learning-based personalization approaches have been introduced in the literature. This paper presents a machine learning personalization approach with the advantage of being efficient in its training based on paired comparisons which makes it practical and field deployable. The training efficiency of this approach is the result of treating frequency bands independent of one another and by simultaneously carrying out Bayesian machine learning in each band across all of the frequency bands. Simulation results indicate that this approach leads to an estimated hearing preference function close to the true hearing preference function in fewer number of paired comparisons relative to the previous machine learning approaches. In addition, a clinical experiment conducted on eight subjects with hearing loss indicate that this training efficient personalization approach provides personalized gain settings which are on average six times more preferred over the standard prescriptive gain settings.