An Environment-Adaptive Management Algorithm for Hearing-Support Devices Incorporating Listening Situation and Noise Type Classifiers

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, Perceptual Evaluation of Speech Quality, 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.

Artificial Organs 2015: A Year in Review

“…A maximum flow velocity of 0.94 m/s was observed at the region downstream of the percutaneous valve at the superior vena cava (SVC). Maximum RSS value of 2076.1 dynes/cm was observed downstream of the valve at the inferior vena cava during the deceleration phase while maximum TKE measured was 572.6 J/m at the upstream of the valve in the SVC during the peak flow phase. While these values appear high, they are significantly lower than those reported in prosthetic mitral and aortic valves.…”

“…Sunhyun Yook et al of Hanyang University, Seoul, Korea investigated a fully automatic hearing‐support (HS) device management algorithm that can adapt to various environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situations and ambient noise type situations with high accuracies.…”

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

“…First, during the subjective evaluations, we considered only one user-controllable noise reduction algorithm: β -adjustable beamforming. However, in commercial devices, additional algorithms may be required to achieve environment-adaptive adjustment, such as single-microphone-based noise-reduction algorithms using spectral subtraction and Wiener filtering [ 17 , 22 ]. To modify the currently implemented algorithm to have a more conventional hearing aid algorithm structure, it is necessary to include additional user-controllable algorithms, such as single-microphone-based noise-reduction, to better reflect users’ personal preferences for various degrees of ambient noise suppression and speech distortion.…”

“…The following assumptions were made for simplification: (1) The device contains two user-adjustable algorithms—beamforming, which adjusts the DNS using the β value, and an output volume adjustment, which adjusts the total output volume of the device using AMP; (2) The wide dynamic-range compression is always active and its channel gains are fixed at purchase; (3) Beamforming is enabled only in speech-in-noise situations and disabled in speech-only and noise-only situations; and (4) The ISL is limited to a 45–90 dB of sound pressure level (dB SPL) range, considering the normal, everyday use presented by Chalupper et al [ 4 ] and Walden et al [ 15 ]. In this study, we used the differential microphone array algorithm suggested by Teutsch and Elko [ 16 ] as the beamforming, and the neural network-based LS classifier suggested by Yook et al [ 17 ] that was slightly modified as follows: (1) Additional amplification in front of the classifier to ensure the proper ISL for stable classification performance and (2) Among the four situations classified in the original LS classifier, only three situations—speech-only, noise-only, and speech-in-noise—were enabled (i.e., music-only was disabled).…”

A Diagonal‐Steering‐Based Binaural Beamforming Algorithm Incorporating a Diagonal Speech Localizer for Persons With Bilateral Hearing Impairment

“…As an alternative, it was implemented to update the value of Thr whenever the voice activity detector does not detect the speech signal for approximately 2 s. However, in this case, when the intensity and the type of environmental noises change drastically while the speech signal enters consistently, the value of Thr cannot be adjusted timely to the proper level and as a result, the localization accuracy and intelligibility of the speech signal may decrease. To overcome this problem, in future studies, more elaborated sound source separation or sound classification algorithms, such as independent component analysis or support vector machine , should be added to the current algorithm to adaptively adjust the value of Thr in accordance with the variations in environmental circumstances.…”