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

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.

Keywords

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