AIMC Topic: Hearing Aids

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Brain benefits of deep learning-based noise management in experienced hearing aid users using functional near infrared spectroscopy.

Scientific reports
There is growing interest in using neuroimaging to understanding listening effort in individuals with hearing loss, with a particular focus on how innovative hearing aid features impact listening effort. This study used functional near infrared spect...

Deep fusion based transfer learning with bald eagle search algorithm for sign language recognition to assist individuals with hearing and speech impairments.

Scientific reports
Sign language (SL) is a significant communication method for individuals with hearing impairments, using hand gestures to convey letters, words, and sentences. However, several people are unfamiliar with SL, creating a communication gap. An intellige...

Deaf futurity: designing and innovating hearing aids.

Medical humanities
One of the tenets of a posthuman vision is the eradication of disability through technology. Within this site of 'no future', as Alison Kafer describes, the disabled body is merged with artificial intelligence technology or transformed into a prosthe...

Use of Hearing Aids Embedded with Inertial Sensors and Artificial Intelligence to Identify Patients at Risk for Falling.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
OBJECTIVE: To compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.

Artificial intelligence enabled smart mask for speech recognition for future hearing devices.

Scientific reports
In recent years, Lip-reading has emerged as a significant research challenge. The aim is to recognise speech by analysing Lip movements. The majority of Lip-reading technologies are based on cameras and wearable devices. However, these technologies h...

Deep learning-based auditory attention decoding in listeners with hearing impairment.

Journal of neural engineering
This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-n...

Optical Microphone-Based Speech Reconstruction System With Deep Learning for Individuals With Hearing Loss.

IEEE transactions on bio-medical engineering
OBJECTIVE: Although many speech enhancement (SE) algorithms have been proposed to promote speech perception in hearing-impaired patients, the conventional SE approaches that perform well under quiet and/or stationary noises fail under nonstationary n...

Restoring speech intelligibility for hearing aid users with deep learning.

Scientific reports
Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep l...

Assessing outcomes of ear molding therapy by health care providers and convolutional neural network.

Scientific reports
Ear molding therapy is a nonsurgical technique to correct certain congenital auricular deformities. While the advantages of nonsurgical treatments over otoplasty are well-described, few studies have assessed aesthetic outcomes. In this study, we comp...