Research to understand hearing function and auditory processing in humans as well as relevant animal species represents translatable work that supports this aim.
[10] More recently, convolutional neural networks (CNNs) have been constructed and trained that can replicate human auditory function[11] or complex cochlear mechanics with high accuracy.
Online pure-tone threshold audiometry (or screening) tests, electrophysiological measures, for example distortion-product otoacoustic emissions (DPOAEs) and speech-in-noise screening tests are becoming increasingly available as a tools to promote awareness and enable accurate early identification of hearing loss across ages, monitor the effects of ototoxicity and/or noise, and guide ear and hearing care decisions and provide support to clinicians.
[16][17] Low-cost earphones attached to smartphones have also been prototyped to help detect the faint otoacoustic emissions from the cochlea and perform neonatal hearing screening.
[31][32] Machine learning has been applied to audiometry to create flexible, efficient estimation tools that do not require excessive testing time to determine someone's individual's auditory profile.
[33][34] Similarly, machine learning based versions of other auditory tests including determining dead regions in the cochlea or equal loudness contours,[35] have been created.