Computational models of the auditory system

Audiological classification performance based on audiological measurements and Common Audiological Functional Parameters (CAFPAs)

Towards the development of a diagnostic supporting tool in audiology, the Common Audiological Functional Parameters (CAFPAs) were shown to be similarly suitable for audiological finding classification as combinations of typical audiological measurements, and thereby provide the potential to combine different audiological databases.

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Use of air conduction thresholds to predict bone conduction asymmetry and air-bone gap

This study used machine learning methods to predict bone conduction abnormalities from air conduction pure tone audiometric thresholds.

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The Panoramic ECAP Method: modelling the electrode-neuron interface in cochlear implant users

The Panoramic ECAP Method models patient-specific electrode-neuron interfaces in cochlear implant users, and may provide important information for optimizing efficacy and improving speech perception outcomes.

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Predicting abnormal hearing difficulty in noise in ‘normal’ hearers using standard audiological measures

This study used machine learning models trained on otoacoustic emissions and audiometric thresholds to predict self-reported difficulty hearing in noise in normal hearers.

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Random Forest Classification to Predict Response to High-Definition Transcranial Direct Current Stimulation Therapy for Tinnitus

A random forest classifier can predict response to high-definition transcranial direct current stimulation treatment for tinnitus with 82.41% accuracy.

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Computational modelling of the human auditory brainstem response to natural speech

The computational model consists of three main parts (auditory nerve, inferior colliculus and cochlear nuclei). The figure shows the input (natural speech) and the neural outputs at the different levels.

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Predicting Hearing Aid Fittings Based on Audiometric and Subject-Related Data: A Machine Learning Approach

A machine learning model is trained on real-world fitting data to predict the user's individual gain based on audiometric and further subject-related data, such as age, gender, and the acoustic environments.

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Aladdin: Automatic LAnguage-independent Development of the Digits-In-Noise test

The Automatic LAnguage-independent Development of the Digits-In-Noise test (Aladdin)-project aims to create a fully automatic test development procedure for digit-in-noise hearing tests in various languages and for different target populations.

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Computational Audiology: new ways to address the global burden of hearing loss
Source: https://www.stripepartners.com/our_writing_article/the-age-of-the-ear/

Computational Audiology: new ways to address the global burden of hearing loss

Computational audiology, the augmentation of traditional hearing health care by digital methods, has potential to dramatically advance audiological precision and efficiency to address the global burden of hearing loss.

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