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An Environment‐Adaptive Management Algorithm for Hearing‐Support Devices Incorporating Listening Situation and Noise Type Classifiers
Author(s) -
Yook Sunhyun,
Nam Kyoung Won,
Kim Heepyung,
Hong Sung Hwa,
Jang Dong Pyo,
Kim In Young
Publication year - 2015
Publication title -
artificial organs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.684
H-Index - 76
eISSN - 1525-1594
pISSN - 0160-564X
DOI - 10.1111/aor.12391
Subject(s) - intelligibility (philosophy) , hearing aid , computer science , active listening , algorithm , classifier (uml) , noise reduction , sound quality , speech recognition , noise (video) , adaptive algorithm , artificial intelligence , audiology , psychology , communication , image (mathematics) , medicine , philosophy , epistemology
Abstract In order to provide more consistent sound intelligibility for the hearing‐impaired person, regardless of environment, it is necessary to adjust the setting of the hearing‐support ( HS ) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening‐situation classifier, a noise‐type classifier, an adaptive noise‐reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms—beamforming, noise‐reduction, and feedback cancellation—and can also adjust internal gains and parameters of the wide‐dynamic‐range compression ( WDRC ) and noise‐reduction ( NR ) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8–96.4% and 90.9–99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal‐to‐noise ratio ( SNR ), frequency‐weighted segmental SNR , P erceptual E valuation of S peech Q uality, and mean opinion test scores of 10 normal‐hearing volunteers of the adaptive multiband spectral subtraction ( MBSS ) algorithm were improved by 1.74 dB , 2.11 dB , 0.49, and 0.68, respectively, compared to the conventional fixed‐parameter MBSS algorithm. These results indicate that the proposed environment‐adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing‐impaired individuals in various acoustic environments.