Purdue University
This study is designed to understand how different hearing-aid noise-reduction technologies affect a listener's ability to hear speech in noisy environments. Participants will listen to speech at several background-noise levels while trying different processing settings. By comparing performance across these conditions, the study aims to identify which types of noise reduction improve speech intelligibility the most. We expect that some noise-reduction strategies will help listeners understand speech better than others, especially in more difficult listening situations.
\*\*\* STUDY DESCRIPTION \*\*\* Hearing in noisy environments is one of the most common challenges faced by individuals with hearing loss, and even people with normal hearing often struggle to understand speech in situations such as restaurants, classrooms, and busy public spaces. Modern hearing aids use advanced digital signal-processing strategies, especially deep neural network (DNN)-based noise reduction, to improve speech intelligibility in these difficult listening situations. However, these technologies vary widely in how well they work, and their benefits can depend on factors such as…
Inclusion Criteria: * A hearing aid candidate with mild-to-moderate cochlear hearing loss, based on audiometric profile (at least 20 dB of hearing loss at 2000 Hz, with progressively worse hearing levels at higher frequencies). Exclusion Criteria: * Normal hearing * Severe or profound hearing loss * Conductive hearing loss * Neural hearing loss
No neural noise suppression applied. Baseline processing condition.
Neural noise suppression using the lower-strength algorithm parameters.
Neural noise suppression using the higher-strength algorithm parameters.
Noise levels higher than speech levels
Equal speech and noise levels
Speech levels higher than noise levels
Arctuva estimate
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