Analysis of individual preferences for tuning of noise-reduction algorithms

The parameters of noise-reduction algorithms in consumer products, such as hearing aids, are often preset using a generic model to represent the average listener. This research explores the degree to which tunable parameters improve the trade-off between noise reduction and speech degradation. A logistic probability model was developed that allowed for interpolation of the data so that the optimal trade-off for each individual subject could be determined. For five out of ten subjects, their preferred settings were modestly different from the parameters obtained using group averages. Nevertheless, at least for normal hearing listeners, individualization does not appear to be critical. Hearing-impaired listeners may acquire more benefit from custom tuning.