Auditory model-based parameter estimation and selection of the most informative experimental conditions

Anna Dietze, Anna-Lena Reinsch, Jörg Encke, Mathias Dietz

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Abstract

Identifying the causes underlying a person’s hearing impairment is challenging. It requires linking the results of listening tests to possible pathologies of the highly non-linear auditory system. This process is further aggravated by restrictions in measurement time, especially in clinical settings. A central but difficult goal is thus, to maximize the diagnostic information that is collectable within a given time frame. This study demonstrates the practical applicability of the model-based experiment-steering procedure introduced in Herrmann and Dietz (2021, Acta Acustica, 5:51). The approach chooses the stimuli that are presented and estimates the model parameters best predicting the subject’s performance using a maximum-likelihood method. The same binaural tone-in-noise detection task was conducted using two measurement procedures: A standard adaptive staircase procedure and the model-based selection procedure based on an existing model. The model-steered procedure reached the same accuracy of model parameter estimation in on average only 42% of the time that was required with the standard adaptive procedure. Difficulties regarding the choice of a reliable model and reasonable discretization steps of its parameters are discussed. Although the physiological causes of an individual’s results cannot directly be inferred using this procedure, a characterization in terms of functional parameters is possible.
Original languageEnglish
Article number3
Pages (from-to)1-12
Number of pages12
JournalActa Acustica
Volume8
DOIs
Publication statusPublished - 9 Jan 2024
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • binaural hearing
  • tone-in-noise detection
  • computational audiology
  • model-based experiment steering
  • audiological diagnostics

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