Robust auditory profiling: Improved data-driven method and profile definitions for better hearing rehabilitation

Authors

  • Raul H Sanchez-Lopez Hearing Systems Section, Dept. of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark https://orcid.org/0000-0002-5239-2339
  • Michal Fereczkowski Hearing Systems Section, Dept. of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark; Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
  • Tobias Neher Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark http://orcid.org/0000-0002-1107-9274
  • Sébastien Santurette Hearing Systems Section, Dept. of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark; Oticon A/S, Smørum, Denmark https://orcid.org/0000-0002-6868-5734
  • Torsten Dau Hearing Systems Section, Dept. of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark

Keywords:

hearing loss, data-driven profiling, hearing rehabilitation, hearing deficits, supra-threshold distortion

Abstract

Currently, the clinical characterization of hearing deficits for hearing-aid fitting is based on the pure-tone audiogram only. This relies on the assumption that the audiogram can predict performance in complex, supra-threshold tasks. Sanchez-Lopez et al. (2018) hypothesized that the hearing deficits of a given listener, both at threshold and supra-threshold levels, result from two independent types of auditory distortions. The authors performed a data-driven analysis of two large datasets with results from several tests, which led to the identification of four auditory profiles. However, the definition of the two types of distortion was challenged by differences between the two datasets in terms of the tests and listeners considered. In the Better hEAring Rehabilitation (BEAR) project, a new dataset was generated with the aim of overcoming these limitations. A heterogeneous group of listeners was tested using measures of speech intelligibility, loudness perception, binaural processing abilities and spectro-temporal resolution. As a consequence, the auditory profiles of Sanchez-Lopez et al. (2018) were refined. The updated auditory profiles, together with the investigation of optimal hearing-aid compensation strategies, are expected to form a solid basis for improved hearing-aid fitting.

References

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Auditory Learning in Biological and Artificial Systems

Additional Files

Published

2020-05-13

How to Cite

Sanchez-Lopez, R. H., Fereczkowski, M., Neher, T., Santurette, S., & Dau, T. (2020). Robust auditory profiling: Improved data-driven method and profile definitions for better hearing rehabilitation. Proceedings of the International Symposium on Auditory and Audiological Research, 7, 281–288. Retrieved from https://proceedings.isaar.eu/index.php/isaarproc/article/view/2019-32

Issue

Vol. 7 (2019): Auditory Learning in Biological and Artificial Systems

Section

2019/4. Novel directions in hearing-instrument technology

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