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Evaluating a Deep Neural Noise-Reduction Algorithm for Hearing Aids

NCT07287774 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 50

Last updated 2025-12-17

No results posted yet for this study

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.

Conditions

  • Hearing Loss, Cochlear

Interventions

DEVICE

Hearing Aid Noise Reduction - Off

No neural noise suppression applied. Baseline processing condition.

DEVICE

Hearing Aid Noise Reduction - Low

Neural noise suppression using the lower-strength algorithm parameters.

DEVICE

Hearing Aid Noise Reduction - High

Neural noise suppression using the higher-strength algorithm parameters.

OTHER

Negative SNR

Noise levels higher than speech levels

OTHER

Zero signal-to-noise ratio

Equal speech and noise levels

OTHER

Positive SNR

Speech levels higher than noise levels

Sponsors & Collaborators

  • Oticon

    collaborator UNKNOWN

  • Purdue University

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
DOUBLE
Model
CROSSOVER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-10-16
Primary Completion
2026-04-30
Completion
2026-04-30
FDA Device
Yes

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07287774 on ClinicalTrials.gov