A major problem of remote or online hearing tests is the calibration of the equipment and control of the acoustic environment: For example, an audiogram requires calibrated headphones with a wide dynamic range and a silent room. Instead, a notched-noise test alleviates these problems and gives valuable information about auditory filters that can be used to fit a hearing aid. Although absolute levels still play a role, relative levels between signal and noise are more robust as the environment does not need to be quieter than the noise. Using traditional methods, determination of auditory-filter shapes across the whole frequency range would take hours. We developed a notched-noise test that uses Gaussian Processes to maintain a probabilistic estimate of the outcome, and to select stimulus parameters based on mutual information. This active-learning method gives an estimate of auditory-filter shapes in about 30 minutes across a wide range of frequency. We demonstrate that these estimates, even when obtained with headphones for which the frequency response and exact calibration is unknown, are sufficient to give a good initial fit of a hearing aid.
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April 2021
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April 01 2021
A Bayesian active-learning test for remote hearing-aid fitting
Josef Schlittenlacher;
Josef Schlittenlacher
Univ. of Manchester, Oxford Rd., Manchester M139PL, United Kingdom[email protected]
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Emanuele Perugia
Emanuele Perugia
Univ. of Manchester, Manchester, United Kingdom
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Josef Schlittenlacher
Karolina Kluk
Michael Stone
Emanuele Perugia
Univ. of Manchester, Oxford Rd., Manchester M139PL, United Kingdom[email protected]
J. Acoust. Soc. Am. 149, A112–A113 (2021)
Citation
Josef Schlittenlacher, Karolina Kluk, Michael Stone, Emanuele Perugia; A Bayesian active-learning test for remote hearing-aid fitting. J. Acoust. Soc. Am. 1 April 2021; 149 (4_Supplement): A112–A113. https://doi.org/10.1121/10.0004683
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