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Evaluating a Deep Neural Noise-Reduction Algorithm for Hearing Aids
Sponsor: Purdue University
Summary
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.
Official title: Evaluating a Deep Neural Noise-Reduction Algorithm for Hearing Aids in Varying Signal-to-Noise Conditions
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
50
Start Date
2025-10-16
Completion Date
2026-04-30
Last Updated
2025-12-17
Healthy Volunteers
No
Conditions
Interventions
Hearing Aid Noise Reduction - Off
No neural noise suppression applied. Baseline processing condition.
Hearing Aid Noise Reduction - Low
Neural noise suppression using the lower-strength algorithm parameters.
Hearing Aid Noise Reduction - High
Neural noise suppression using the higher-strength algorithm parameters.
Negative SNR
Noise levels higher than speech levels
Zero signal-to-noise ratio
Equal speech and noise levels
Positive SNR
Speech levels higher than noise levels
Locations (1)
Purdue University
West Lafayette, Indiana, United States