Clin Exp Otorhinolaryngol.
2017 Mar;10(1):56-65. 10.21053/ceo.2015.01690.
A Trainable Hearing Aid Algorithm Reflecting Individual Preferences for Degree of Noise-Suppression, Input Sound Level, and Listening Situation
- Affiliations
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- 1Institute of Biomedical Engineering, Hanyang University, Seoul, Korea. iykim@hanyang.ac.kr
- 2Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
- 3Department of Medical Device Management & Research, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Korea.
- 4S/W Solution Lab, Samsung Advanced Institute of Technology, Yongin, Korea.
- 5Department of Otolaryngology-Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
- KMID: 2376409
- DOI: http://doi.org/10.21053/ceo.2015.01690
Abstract
OBJECTIVES
In an effort to improve hearing aid users' satisfaction, recent studies on trainable hearing aids have attempted to implement one or two environmental factors into training. However, it would be more beneficial to train the device based on the owner's personal preferences in a more expanded environmental acoustic conditions. Our study aimed at developing a trainable hearing aid algorithm that can reflect the user's individual preferences in a more extensive environmental acoustic conditions (ambient sound level, listening situation, and degree of noise suppression) and evaluated the perceptual benefit of the proposed algorithm.
METHODS
Ten normal hearing subjects participated in this study. Each subjects trained the algorithm to their personal preference and the trained data was used to record test sounds in three different settings to be utilized to evaluate the perceptual benefit of the proposed algorithm by performing the Comparison Mean Opinion Score test.
RESULTS
Statistical analysis revealed that of the 10 subjects, four showed significant differences in amplification constant settings between the noise-only and speech-in-noise situation (P<0.05) and one subject also showed significant difference between the speech-only and speech-in-noise situation (P<0.05). Additionally, every subject preferred different β settings for beamforming in all different input sound levels.
CONCLUSION
The positive findings from this study suggested that the proposed algorithm has potential to improve hearing aid users' personal satisfaction under various ambient situations.
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