AI Medical Compendium Journal:
Trends in hearing

Showing 1 to 9 of 9 articles

Neural-WDRC: A Deep Learning Wide Dynamic Range Compression Method Combined With Controllable Noise Reduction for Hearing Aids.

Trends in hearing
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
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...

ADT Network: A Novel Nonlinear Method for Decoding Speech Envelopes From EEG Signals.

Trends in hearing
Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods stru...

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...

New Avenues in Audio Intelligence: Towards Holistic Real-life Audio Understanding.

Trends in hearing
Computer audition (i.e., intelligent audio) has made great strides in recent years; however, it is still far from achieving holistic hearing abilities, which more appropriately mimic human-like understanding. Within an audio scene, a human listener i...

Perceptual Effects of Adjusting Hearing-Aid Gain by Means of a Machine-Learning Approach Based on Individual User Preference.

Trends in hearing
This study investigated a method to adjust hearing-aid gain by use of a machine-learning algorithm that estimates the optimal setting of gain parameters based on user preference indicated in an iterative paired-comparison procedure. Twenty hearing-im...

Analyzing the Time Course of Pupillometric Data.

Trends in hearing
This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about p...

Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality.

Trends in hearing
Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using...

Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms.

Trends in hearing
The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid alg...