AIMC Topic: Hearing Loss

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Anatomical Considerations for Achieving Optimized Outcomes in Individualized Cochlear Implantation.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
HYPOTHESIS: Machine learning models can assist with the selection of electrode arrays required for optimal insertion angles.

Towards Investigating Residual Hearing Loss: Quantification of Fibrosis in a Novel Cochlear OCT Dataset.

IEEE transactions on bio-medical engineering
OBJECTIVE: Cochlear implants (CIs) are bionic prostheses that restores hearing via electrical stimulation of the auditory nerve. Hybrid CIs, which use electroacoustic stimulation (EAS), combine residual low-frequency acoustic hearing with CI electric...

Machine learning-based prediction of hearing loss: Findings of the US NHANES from 2003 to 2018.

Hearing research
The prevalence of hearing loss (HL) has emerged as an escalating public health concern globally. The objective of this study was to leverage data from the National Health and Nutritional Examination Survey (NHANES) to develop an interpretable predict...

Piezoelectric nanofiber-based intelligent hearing system.

Science advances
Hearing loss, affecting individuals of all ages, can impair education, social function, and quality of life. Current treatments, such as hearing aids and implants, aim to mitigate these effects but often fall short in addressing the critical issue of...

Deep learning-based classification of speech disorder in stroke and hearing impairment.

PloS one
BACKGROUND AND OBJECTIVE: Speech disorders can arise from various causes, including congenital conditions, neurological damage, diseases, and other disorders. Traditionally, medical professionals have used changes in voice to diagnose the underlying ...

Using deep learning to improve the intelligibility of a target speaker in noisy multi-talker environments for people with normal hearing and hearing loss.

The Journal of the Acoustical Society of America
Understanding speech in noisy environments is a challenging task, especially in communication situations with several competing speakers. Despite their ongoing improvement, assistive listening devices and speech processing approaches still do not per...

Performance and Reliability Evaluation of an Automated Bone-Conduction Audiometry Using Machine Learning.

Trends in hearing
To date, pure-tone audiometry remains the gold standard for clinical auditory testing. However, pure-tone audiometry is time-consuming and only provides a discrete estimate of hearing acuity. Here, we aim to address these two main drawbacks by develo...

Progress made in the efficacy and viability of deep-learning-based noise reduction.

The Journal of the Acoustical Society of America
Recent years have brought considerable advances to our ability to increase intelligibility through deep-learning-based noise reduction, especially for hearing-impaired (HI) listeners. In this study, intelligibility improvements resulting from a curre...

Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods.

Trends in hearing
Frequency-domain monaural speech enhancement has been extensively studied for over 60 years, and a great number of methods have been proposed and applied to many devices. In the last decade, monaural speech enhancement has made tremendous progress wi...

Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.

The Journal of the Acoustical Society of America
The practical efficacy of deep learning based speaker separation and/or dereverberation hinges on its ability to generalize to conditions not employed during neural network training. The current study was designed to assess the ability to generalize ...