A Trainable Hearing Aid Algorithm Reflecting Individual Preferences for Degree of Noise-Suppression, Input Sound Level, and Listening Situation
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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.
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.
참고문헌 (24)
- WHO global estimates on prevalence of hearing loss: morality and burden of diseases and prevention of blindness and deafness WHO, "World Health Organization", World Health Organization, ~ (2012)
- McPherson B., "Innovative technology in hearing instruments: matching needs in the developing world", Trends Amplif, 15(4), pp.209~214 (2011)
- Kochkin S., "MarkeTrak VII: customer satisfaction with hearing instruments in the digital age", Hearing J, 58(9), pp.30~ (2005)
- Chalupper J, "Algorithm lets users train aid to optimize compression, frequency shape, and gain", Hearing J, 62(8), pp.26~ (2009)
- Lamarche L, "Adaptive environment classification system for hearing aids", J Acoust Soc Am, 127(5), pp.3124~3135 (2010)
- Taylor B., "Advanced user control optimizes real-world listening preferences", Hearing J, 64(3), pp.26~ (2011)
- Dillon H, "The trainable hearing aid : what will it do for clients and clinicians?", Hearing J, 59(4), pp.30~ (2006)
- Kochkin S., "MarkeTrak VII: obstacles to adult non-user adoption of hearing aids", Hearing J, 60(4), pp.24~51 (2007)
- Dreschler WA, "Client-based adjustments of hearing aid gain: the effect of different control configurations", Ear Hear, 29(2), pp.214~227 (2008)
- Boymans M, "Audiologist-driven versus patient-driven fine tuning of hearing instruments", Trends Amplif, 16(1), pp.49~58 (2012)
- Zakis JA, "The design and evaluation of a hearing aid with trainable amplification parameters", Ear Hear, 28(6), pp.812~830 (2007)
- Keidser G, "The effect of the base line response on self-adjustments of hearing aid gain", J Acoust Soc Am, 124(3), pp.1668~1681 (2008)
- Keidser G, "Real-life efficacy and reliability of training a hearing aid", Ear Hear, 34(5), pp.619~629 (2013)
- Nielsen JB, "Perception-based personalization of hearing aids using Gaussian processes and active learning", IEEE, 2015, pp.23(1), 162~173
- Walden BE, "Effect of signal-to-noise ratio on directional microphone benefit and preference", J Am Acad Audiol, 16(9), pp.662~676 (2005)
- Teutsch H, "First- and second-order adaptive differential microphone arrays", Proceedings of the 7th International Workshop on Acoustic Echo and Noise Control, pp.35~38 (2001)
- Yook S, "An environment-adaptive management algorithm for hearing-support devices incorporating listening situation and noise type classifiers", Artif Organs, 39(4), pp.361~368 (2015)
- Schaub A., "Digital hearing aids" (2008), Thieme Medical Publishers Inc
- Buchler M, "Sound classification in hearing aids inspired by auditory scene analysis", EURASIP J Appl Signal Process, 18, pp.2991~3002 (2005)
- "P.800: methods for subjective determination of transmission quality" (2008)[URL]
- Ricketts TA, "Sound quality measures for speech in noise through a commercial hearing aid implementing digital noise reduction", J Am Acad Audiol, 16(5), pp.270~277 (2005)
- Mueller HG, "Using trainable hearing aids to examine real-world preferred gain", J Am Acad Audiol, 19(10), pp.758~773 (2008)
- Byrne D, "NAL-NL1 procedure for fitting nonlinear hearing aids: characteristics and comparisons with other procedures", J Am Acad Audiol, 12(1), pp.37~51 (2001)
- Pierce JL, "The state of psychological ownership:integrating and extending a century of research", Rev Gen Psychol, 7(1), pp.84~107 (2003)