Personalization of Hearing Aid Fitting Based on Adaptive Dynamic Range Optimization.

Aoxin Ni, Sara Akbarzadeh, Edward Lobarinas, Nasser Kehtarnavaz

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids.Sensors (Basel, Switzerland) · 2024
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Aoxin NiDepartment of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080-3021, USA.

Sara AkbarzadehDepartment of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080-3021, USA.

Edward LobarinasCallier Center for Communication Disorders, University of Texas at Dallas, Richardson, TX 75080-3021, USA.

Nasser KehtarnavazDepartment of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080-3021, USA.

Funding

Hearing Lab Technology, LLC Project id 9478

6 · The paper itself

Abstract

Adaptive dynamic range optimization (ADRO) is a hearing aid fitting rationale which involves adjusting the gains in a number of frequency bands by using a series of rules. The rules reflect the comparison of the estimated percentile occurrences of the sound levels with the audibility and comfort hearing levels of a person suffering from hearing loss. In the study reported in this paper, a previously developed machine learning method was utilized to personalize the ADRO fitting in order to provide an improved hearing experience as compared to the standard ADRO hearing aid fitting. The personalization was carried out based on the user preference model within the framework of maximum likelihood inverse reinforcement learning. The testing of ten subjects with hearing loss was conducted, which indicated that the personalized ADRO was preferred over the standard ADRO on average by about 10 times. Furthermore, a word recognition experiment was conducted, which showed that the personalized ADRO had no adverse impact on speech understanding as compared to the standard ADRO.

Indexed as

Cochlear ImplantsDeafnessHearing AidsHearing LossSpeech PerceptionHumansadaptive dynamic range optimizationmaximum likelihood inverse reinforcement learningpersonalization of hearing aid fitting

Identifiers

PMID36015791

DOI10.3390/s22166033

PMCPMC9414822

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

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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.