An Appraisal of the Advancement of Emerging Technologies in Hearing Aids

Rehman, Muhammad Zia ur; Shah, Syed Irtiza Ali; Gilani, Syed Omer; Jamil, Mohsin; Amin, Faisal Mohammad

doi:10.17485/ijst/2016/v9i25/96607

Cited by 5 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ever since 1996, when digital signal processing was introduced for the first time 39 , hearing technology has advanced over the years to aid HA users in understanding speech in noise better than they were able to before. Examples include directional microphones, NR, ear-to-ear communication, hearing aid accessories, and so on 40,41 . However, even with the help of HAs, due to various reasons (i.e.…”

Section: Discussionmentioning

confidence: 99%

Impact of hearing aid noise reduction algorithms on the speech-evoked auditory brainstem response

Seol

Park

et al. 2020

Sci Rep

View full text Add to dashboard Cite

the purposes of this study are to investigate the neural representation of a speech stimulus in the auditory system of individuals with normal hearing (NH) and those with hearing aids (HAs) and to explore the impact of noise reduction algorithms (NR) on auditory brainstem response to complex sounds (cABR). Twenty NH individuals and 28 HA users completed puretone audiometry, the Korean version of the Hearing in Noise Test (K-HINT), and cABR. In 0 and +5 dB signal-to-noise ratios (SNRs), the NH group was tested in /da/ only (quiet) and /da/ with white noise (WN) conditions while the HA group was tested in /da/ only, /da/ WN, /da/ WN NR ON, and /da/ WN NR OFF conditions. Significant differences were observed between /da/ only and /da/ WN conditions for F0 in both groups, but no SNR effect was observed for both groups. Findings of this study are consistent with previous literature that diminished cABR amplitudes indicate reduced representation of sounds in the auditory system. This is the first to examine the effect of a specific HA feature on cABR responses. Hearing loss is a public health problem; the World Health Organization reports that 466 million people around the world experience disabling hearing loss 1. For those who are diagnosed with sensorineural hearing loss, HAs that amplify acoustic signals are often recommended as a major rehabilitative option. HAs should be fitted appropriately based on each patient's audiogram and characteristics, and a typical HA fitting appointment involves verification and validation. Patients are typically fitted with HAs with the aim of improving speech recognition in both quiet and noisy environments. Therefore, HAs contain features, such as NR, directional microphones, speech enhancement, or frequency lowering, to increase speech intelligibility. Speech intelligibility is one's ability to hear and understand speech signals. It is a natural, but complex process as both peripheral and central systems are involved 2-5. Compared to speech recognition in noise, speech recognition in a quiet environment is less challenging for individuals with HAs 6,7. When noise is present, speech signals get distorted, so it becomes challenging even for individuals with NH to understand speech 6,8. HAs have been shown to improve speech intelligibility in individuals with hearing loss. Hallgren, Larsby, Lyxell, and Arlinger (2005) compared the listening effort of HA users' aided and unaided performance in quiet and noisy environments. Perceived effort was lower and word identification scores were higher with HA use 6. However, there is a lack of evidence if various technical features employed in HAs are truly beneficial for speech understanding in noise. Brons, Houben, and Dreschler (2014) examined the effect of single-microphone NR on twenty individuals with sensorineural hearing loss. Although NR decreased annoyance caused by noise, it did not increase speech intelligibility; one of the randomly coded HA recordings in the study, NR2, was preferred the most by the participants, but showed the lo...

show abstract

Section: Discussionmentioning

confidence: 99%

Impact of hearing aid noise reduction algorithms on the speech-evoked auditory brainstem response

Seol

Park

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…After the introduction of digital signal processing in 1996, hearing technology has greatly advanced [ 16 ]. The advances include HA accessories, noise reduction, wireless connectivity, and directional microphones [ 17 , 18 ]. Instead of disposable batteries, rechargeable batteries are now available for most HAs [ 19 ].…”

Section: Hearing Aidsmentioning

confidence: 99%

Hearables as a Gateway to Hearing Health Care: A Review

Seol

Moon

2022

Clin Exp Otorhinolaryngol

View full text Add to dashboard Cite

show abstract

“…Better Lung segmentation method will improve performance CAD system. The intensity based threshold has chosen as an approach segmentation method in this paper (Zia ur Rehman et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Mproved Sauvola Threshold for Background Subtraction on Moving Object Detection

Pambudi

2019

IJSECS

View full text Add to dashboard Cite

Image Segmentation is one essential processing on moving object detection. The one of common segmentation methods is thresholding. In this paper, Thresholding method based on adaptive local technique using local mean and standard deviation is known as 'WAN' method. WAN has been inspired by the Sauvola's binarization method and exhibits its robustness and effectiveness when evaluated on low quality document images. The objective of the WAN to enhance the sauvola method and to get a better binarization result and enhance the accuracy. This research aims to produce output value of WAN algorithm. WAN would be compared to other existing adaptive local method like sauvola and niblack. This research is implemented by using matlab and four videos original from camera. The best result calculation error (MSE,PSNR) of WAN method are (0.0011, 53,6655). Overall, the result of WAN method in this paper is more effective and efficient than the other existing method based on MSE and PSNR.

show abstract

An Appraisal of the Advancement of Emerging Technologies in Hearing Aids

Cited by 5 publications

References 6 publications

Impact of hearing aid noise reduction algorithms on the speech-evoked auditory brainstem response

Impact of hearing aid noise reduction algorithms on the speech-evoked auditory brainstem response

Hearables as a Gateway to Hearing Health Care: A Review

Mproved Sauvola Threshold for Background Subtraction on Moving Object Detection

Contact Info

Product

Resources

About