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Adaptive spatial filtering improves speech reception in noise while preserving binaural cues.
Susan R S Bissmeyer1, Raymond L Goldsworthy1
1Caruso Department of Otolaryngology, Caruso Center for Childhood Communication, University of Southern California, 806 West Adams Boulevard, Los Angeles, California 90007, USA.
The Journal of the Acoustical Society of America
|October 2, 2017
Summary
A new binaural Fennec algorithm enhances speech comprehension for hearing aid users in noisy environments. This noise reduction technology improves hearing by preserving crucial binaural cues for sound localization.
Area of Science:
- Audiology
- Signal Processing
- Hearing Science
Background:
- Hearing loss impairs speech understanding in noise.
- Existing signal-processing algorithms aim to improve speech reception for cochlear implant and hearing aid users.
- A key challenge is noise reduction without degrading binaural hearing through interaural distortion.
Purpose of the Study:
- To evaluate the binaural Fennec noise reduction algorithm.
- To assess its effectiveness in improving speech reception in background noise.
- To determine if it preserves binaural cues essential for hearing.
Main Methods:
- Speech reception thresholds were measured in normal-hearing listeners.
- Listeners were exposed to target speech and noise from specific directions in simulated anechoic and reverberant environments.
- Lateralization thresholds were also measured under similar conditions.
Main Results:
- The binaural Fennec algorithm significantly improved speech reception thresholds, even in reverberant conditions.
- The algorithm enhanced lateralization thresholds in anechoic settings.
- Lateralization thresholds remained unaffected in reverberant environments.
Conclusions:
- The binaural Fennec algorithm effectively improves speech reception in background noise.
- It successfully preserves binaural cues necessary for sound lateralization.
- This technology offers a promising solution for individuals with hearing loss experiencing difficulties in noisy environments.
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