A Trainable Hearing Aid Algorithm Reflecting Individual Preferences for Degree of Noise-Suppression, Input Sound Level, and Listening Situation

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.