New York, New York 10016

  • Hearing Loss

Purpose:

When hearing-impaired listeners are properly aided with a hearing aid (HA) or cochlear implant (CI), they are often able to comfortably maintain a conversation in quiet environments. However, in group environments, such as a large family dinner, restaurant, or other environment where multiple people are talking simultaneously, hearing-impaired listeners have great difficulty participating in conversations and frequently withdraw or avoid the situation. As such, it would be highly beneficial to implement an algorithm into HAs or CIs to remove background talkers ("babble") from the signal to reduce listening effort for the hearing-impaired listener and allow them to converse as if they were in a quiet environment. Although HAs and CIs frequently incorporate noise reduction algorithms, these algorithms are not effective when the background is babble. The problem of removing babble involves segregating speech from speech. Hence, the spectral properties of the signal and noise are extremely similar. Despite these challenges, we developed an algorithm to remove background babble. In the following study will test the ability of cochlear implant users to understand speech with background babble noise using our noise reduction algorithm or no noise reduction algorithm. We hypothesize that CI users will be able to understand significantly more speech in babble noise when using our algorithm.


Criteria:

Inclusion Criteria: Subjects with CIs 12 years of age and older with no diagnosis of other communicative or cognitive disorders. Subjects must have native or native-like English proficiency. Exclusion Criteria: No diagnosed cognitive or communicative disorders (other than hearing loss), presence of acute/chronic otitis media, useable acoustic hearing, and non-English speaking.


NCT ID:

NCT04777669


Primary Contact:

N/A


Backup Contact:

N/A


Location Contact:

New York, New York 10016
United States

Natalia Stupak, AuD
Phone: 646-501-4153
Email: Natalia.Stupak@nyulangone.org

Site Status: Recruiting


Data Source: ClinicalTrials.gov

Date Processed: June 12, 2021

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