Sound personalization is very beneficial, especially for users with impaired hearing. However, classical fitting strategies for hearing instruments involving extensive audiological measurements and fine tuning by trained experts are not applicable for consumer devices such as headphones. This study therefore investigates different user-interface designs enabling users to self-adjust an effective hearing-support algorithm to their individual needs. The large number of parameters of nonlinear and frequency-dependent processing requires a significant reduction of the degrees of freedom, which in this study is achieved by a systematical development of presets covering a large range of typical frequency-dependent hearing-threshold elevations. Experimental validations with hearing-impaired listeners are conducted to assess fitting duration, reproducibility, sound preference, and usability. The results show that a fast and reliable self-fitting is possible when interfaces are used which enable the user to modify algorithmic parameters in real-time in an intuitive and easy way. Despite having very similar kinds of hearing loss, the listeners differ widely in their preferred parameter selection, indicating that self-adjusted sound personalization has a large potential for increasing user satisfaction.
https://www.aes.org/e-lib/browse.cfm?elib=18370
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