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

• Yoon SH
• Nam KW
• Yook S
• Cho BH
• Jang DP
• Hong SH
• Kim IY

• 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.

Keyword

Hearing Aid; Classification; Patient Preference; Digital Signal Processing

MeSH Terms

Acoustics
Classification
Hearing Aids*
Hearing*
Humans
Noise
Patient Preference
Personal Satisfaction
Signal Processing, Computer-Assisted

Figure

• 

  Fig. 1. Overall schematic of the proposed algorithm for a trainable hearing aid that can reflect the variations in all of the ISL, LS, and DNS conditions. MIC, microphone; S/I, signal interface; U/I, user interface; LS, listening situation; ISL, input sound level; DNS, degree of noise suppression; NO, normal operation; BF, beamforming; WDRC, wide dynamic-range compression; OVA, output volume adjustment; AMP, amplification constant in OVA; DB, database.

• 

  Fig. 2. Amplification constant (AMP) variation patterns when both training and verification were performed using TS_1 (unit: dB): (A) Speech-only. (B) Noise-only. (C) Speech-in-noise. Dotted line indicates data collected during training. Solid line indicates amplification output by the proposed algorithm. SPL, sound pressure level.

• 

  Fig. 3. Amplification constant (AMP) variation patterns when training was performed using TS_1 and verification using TS_2 (unit: dB): (A) Speech-only. (B) Noise-only. (C) Speech-in-noise. Dotted line indicates data collected during training. Solid line indicates amplification output by the proposed algorithm. SPL, sound pressure level.

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