AIMC Topic: Auditory Threshold
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Deep Learning Models for Predicting Hearing Thresholds Based on Swept-Tone Stimulus-Frequency Otoacoustic Emissions.
Ear and hearing
Nov 22, 2023
OBJECTIVES: This study aims to develop deep learning (DL) models for the quantitative prediction of hearing thresholds based on stimulus-frequency otoacoustic emissions (SFOAEs) evoked by swept tones.
Predicting speech discrimination scores from pure-tone thresholds-A machine learning-based approach using data from 12,697 subjects.
PloS one
Dec 31, 2021
Diagnostic tests for hearing impairment not only determines the presence (or absence) of hearing loss, but also evaluates its degree and type, and provides physicians with essential data for future treatment and rehabilitation. Therefore, accurately ...
Speech Perception
Auditory Threshold
Discrimination Learning
Speech Reception Threshold Test
Computational Biology
Models, Statistical
Audiometry, Pure-Tone
Hearing
Hearing Loss
Algorithms
Aged
Republic of Korea
Aged, 80 and over
Female
Young Adult
Humans
Adult
Infant, Newborn
Neural Networks, Computer
Middle Aged
Male
Machine Learning
Infant
Adolescent
Child, Preschool
Child
Reproducibility of Results
Robust machine learning method for imputing missing values in audiograms collected in children.
International journal of audiology
Feb 27, 2021
OBJECTIVE: To assess the accuracy and reliability of a machine learning (ML) algorithm for predicting the full audiograms of hearing-impaired children relative to the common approach (CA).
From manual to artificial intelligence fitting: Two cochlear implant case studies.
Cochlear implants international
Sep 17, 2019
To assess whether CI programming by means of a software application using artificial intelligence (AI), FOX®, may improve cochlear implant (CI) performance. Two adult CI recipients who had mixed auditory results with their manual fitting were selec...
Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study.
PloS one
Jun 3, 2019
We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearin...
Objective auditory brainstem response classification using machine learning.
International journal of audiology
Jan 21, 2019
OBJECTIVE: The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: 'clear response', 'inconclusive' or 'response absent'.
A neural-based vocoder implementation for evaluating cochlear implant coding strategies.
Hearing research
Jan 14, 2016
Most simulations of cochlear implant (CI) coding strategies rely on standard vocoders that are based on purely signal processing techniques. However, these models neither account for various biophysical phenomena, such as neural stochasticity and ref...
Signal Processing, Computer-Assisted
Prosthesis Design
Acoustic Stimulation
Auditory Pathways
Cochlear Implantation
Speech Acoustics
Adult
Stochastic Processes
Humans
Auditory Threshold
Male
Electric Stimulation
Neural Networks, Computer
Voice Quality
Middle Aged
Cochlear Implants
Computer Simulation
Loudness Perception
Time Factors
Persons with Hearing Disabilities
Female
Psychoacoustics
Electroencephalography
Speech Perception
Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.
International archives of occupational and environmental health
Nov 29, 2014
PURPOSE: Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using art...
Neural-WDRC: A Deep Learning Wide Dynamic Range Compression Method Combined With Controllable Noise Reduction for Hearing Aids.
Trends in hearing
Jan 1, 2025
Wide dynamic range compression (WDRC) and noise reduction both play important roles in hearing aids. WDRC provides level-dependent amplification so that the level of sound produced by the hearing aid falls between the hearing threshold and the highes...
Performance and Reliability Evaluation of an Automated Bone-Conduction Audiometry Using Machine Learning.
Trends in hearing
Jan 1, 2024
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...
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