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.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
BASIC_SCIENCE
Masking
NONE
Enrollment
100
In within subject design, listeners will have background noise removed by a noise reduction algorithm
New York University School of Medicine
New York, New York, United States
RECRUITINGSpeech Recognition
The % of words correctly understood in various noisy situations will be measured
Time frame: 1 day
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