Optimized loudness-function estimation for categorical loudness scaling data

Abstract

Individual loudness perception can be assessed using categorical loudness scaling (CLS). The procedure does not require any training and is frequently used in clinics. The goal of this study was to investigate different methods of loudness-function estimation from CLS data in terms of their test–retest behaviour and to suggest an improved method compared to Brand and Hohmann (2002) for adaptive CLS. Four different runs of the CLS procedure were conducted using 13 normal-hearing and 11 hearing-impaired listeners. The following approaches for loudness-function estimation (fitting) by minimising the error between the data and loudness function were compared: Errors were defined both in level and in loudness direction, respectively. The hearing threshold level (HTL) was extracted from CLS by splitting the responses into an audible and an inaudible category. The extracted HTL was used as a fixed starting point of the loudness function. The uncomfortable loudness level (UCL) was estimated if presentation levels were not sufficiently high to yield responses in the upper loudness range, as often observed in practise. Compared to the original fitting method, the modified estimation of the HTL was closer to the pure-tone audiometric threshold. Results of a computer simulation for UCL estimation showed that the estimation error was reduced for data sets with sparse or absent responses in the upper loudness range. Overall, the suggested modifications lead to a better test–retest behaviour. If CLS data are highly consistent over the whole loudness range, all fitting methods lead to almost equal loudness functions. A considerable advantage of the suggested fitting method is observed for data sets where the responses either show high standard deviations or where responses are not present in the upper loudness range. Both cases regularly occur in clinical practice.