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多频类别响度标度的参数贝叶斯自适应方法。

Toward parametric Bayesian adaptive procedures for multi-frequency categorical loudness scaling.

机构信息

Department of Speech and Hearing Sciences, University of Washington, 1417 NE 42nd Street, Seattle, Washington 98105, USA.

Boys Town National Research Hospital, Omaha, Nebraska 68131, USA.

出版信息

J Acoust Soc Am. 2024 Jul 1;156(1):262-277. doi: 10.1121/10.0026592.

DOI:10.1121/10.0026592
PMID:38980101
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11240213/
Abstract

A series of Bayesian adaptive procedures to estimate loudness growth across a wide frequency range from individual listeners was developed, and these procedures were compared. Simulation experiments were conducted based on multinomial psychometric functions for categorical loudness scaling across ten test frequencies estimated from 61 listeners with normal hearing and 87 listeners with sensorineural hearing loss. Adaptive procedures that optimized the stimulus selection based on the interim estimates of two types of category-boundary models were tested. The first type of model was a phenomenological model of category boundaries adopted from previous research studies, while the other type was a data-driven model derived from a previously collected set of categorical loudness scaling data. An adaptive procedure without Bayesian active learning was also implemented. Results showed that all adaptive procedures provided convergent estimates of the loudness category boundaries and equal-loudness contours between 250 and 8000 Hz. Performing post hoc model fitting, using the data-driven model, on the collected data led to satisfactory accuracies, such that all adaptive procedures tested in the current study, independent of modeling approach and stimulus-selection rules, were able to provide estimates of the equal-loudness-level contours between 20 and 100 phons with root-mean-square errors typically under 6 dB after 100 trials.

摘要

开发了一系列贝叶斯自适应程序,用于从个体听众中估计宽频率范围内的响度增长,并对这些程序进行了比较。基于类别响度标度的多项心理物理函数,针对 61 名正常听力和 87 名感音神经性听力损失听众的十个测试频率进行了模拟实验。测试了基于两种类型的边界模型的中间估计值优化刺激选择的自适应程序。第一种边界模型是从先前的研究中采用的类别边界现象学模型,而另一种边界模型是从先前收集的一组类别响度标度数据中得出的数据驱动模型。还实现了没有贝叶斯主动学习的自适应程序。结果表明,所有自适应程序都提供了 250 至 8000 Hz 之间的响度类别边界和等响曲线的收敛估计值。对收集的数据进行事后模型拟合,使用数据驱动模型,得到了令人满意的准确性,使得当前研究中测试的所有自适应程序,无论建模方法和刺激选择规则如何,都能够在 100 次试验后,以通常低于 6dB 的均方根误差提供 20 至 100 分贝等响级轮廓的估计值。

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