Feasibility of interleaved Bayesian adaptive procedures in estimating the equal-loudness contour

Abstract

A Bayesian adaptive procedure, the interleaved-equal-loudness contour (IELC) procedure, was developed to improve the efficiency in estimating the equal-loudness contour. Experiment 1 evaluated the test-retest reliability of the IELC procedure using six naive normal-hearing listeners. Two IELC runs of 200 trials were conducted and excellent test-retest reliability was found at both the group and individual levels. Using the same group of listeners, Experiment 2 compared the IELC procedure to two other procedures that required frequency-by-frequency testing. One of these procedures was the commonly adopted interleaved staircase (ISC) procedure from Jesteadt [(1980). Atten. Percept. Psychophys. 28, 85-88]. The other procedure, the interleaved maximum-likelihood (IML) procedure, was a modification of the updated maximum-likelihood procedure [Shen and Richards (2012). J. Acoust. Soc. Am. 132, 957-967]. For each of the ISC and IML procedures, two runs of approximately 500 trials were conducted, followed by one additional IELC run. The testretest reliability of the IELC procedure was comparable to that of the ISC and IML procedure. The accuracies of all three procedures measured in Experiment 2 were similar, which was superior to the accuracies of the IELC runs from Experiment 1, indicating a potential training effect.

Psychophysics with children: Evaluating the use of maximum likelihood estimators in children aged 4–15 years (QUEST+)

“…For example, it would be perfectly possible to fit a more complex spline type model, or to use multiple ML routines to independently estimate thresholds at predefined stimulus location (i.e., as required in the Staircase design). Indeed, the former approach has been used successfully to estimate equal-loudness contours in hearing (Shen, Zhang, & Zhang, 2018), while the latter is the approach currently used in visual field testing (Turpin et al, 2003). However, it is important to note that in doing so, the speed of the technique will be reduced concomitantly, and in the limiting case, there may well be relatively little difference in efficiency between the Staircase and ML procedures-though residual benefits may remain, such as the ability of ML procedures to integrate priors and dynamic stopping rules (Shen et al, 2018).…”

Multi-species numerical validation of an efficient algorithm for auditory brainstem response hearing threshold estimation

“…Bayesian adaptive sampling has also been successfully applied in the auditory domain. For example, it has been used to estimate the auditory filter shape (Shen and Richards, 2013, Shen et al, 2014, Schlittenlacher et al, 2020), the audiogram (Song et al, 2015, Cox and de Vries, 2021), the equal-loudness contour (Shen et al, 2018, Schlittenlacher and Moore, 2020, [Shen et al 2024 JASA under-review]), and the band importance function for speech intelligibility (Shen and Kern, 2018 Jan-Dec, Shen et al, 2020, Shen and Langley, 2023).…”

Application of Bayesian Active Learning to the Estimation of Auditory Filter Shapes Using the Notched-Noise Method

“…For example, GPs have been used to search for the optimal setting of a hearing aid ( Jensen et al., 2019 ; Nielsen et al., 2014 ), and for determining audiograms (Cox & de Vries, 2015; Schlittenlacher et al., 2018a ; Song et al., 2015 ), equal-loudness contours ( Schlittenlacher & Moore, 2020 ), and psychometric functions ( Song et al., 2017 ). Other BAL approaches, often using parametric models but also maximizing mutual information or something similar, have been used to determine equal-loudness contours ( Shen et al., 2018 ) or the edge frequency of a dead region ( Schlittenlacher et al., 2018b ).…”