In<unk> , models and algorithms are used to understand the principles that govern the auditory system, to screen for hearing loss, to diagnose hearing disorders, to provide rehabilitation, and to generate simulations for patient education, among others.<unk>  models of hearing, speech and auditory perception For decades, phenomenological & biophysical<unk>  models have been developed to simulate characteristics of the human auditory system. Examples include models of the mechanical properties of the basilar membrane, the electrically stimulated cochlea, middle ear mechanics, bone conduction, and the central auditory pathway. Saremi et al. (2016) compared 7 contemporary models including parallel filterbanks, cascaded filterbanks, transmission lines and biophysical models. More recently, convolutional neural networks (CNNs) have been constructed and trained that can replicate human auditory function or to reproduce complex cochlear mechanics with high accuracy. Although inspired by the interconnectivity of biological neural networks, the architecture of CNNs is distinct from the organization of the natural auditory system. e-Health / mHealth (connected hearing healthcare, wireless- and internet-based services) Online pure-tone threshold audiometry (or screening) tests, electrophysiological measures, for example distortion-product OAEs (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 support to clinicians. Big data and AI in<unk>  and hearing healthcare Collecting large numbers of audiograms (e.g. the NIOSH or NHANES databases) provides researchers opportunities to find patterns of hearing status in the population or to train AI systems that can classify audiograms. Machine learning can be used to predict the relationship between multiple factors e.g. predict depression based on self-reported hearing loss or the relation between genetic profile and self-reported hearing loss. Hearing aids and wearable provide the option to monitor the soundscape of the user or log the usage patterns which can be used to automatically recommend settings that are expected to benefit the user.<unk>  approaches to improving hearing devices and auditory implants Methods to improve rehabilitation by auditory implants include improving music perception, models of the electrode-neuron interface, and an AI based Cochlear Implant fitting assistant. Data-based investigations into hearing loss and tinnitus Online surveys processed with ML-based classification have been used to diagnose somatosensory tinnitus. Automated NLP techniques, unsupervised and supervised Machine Learning have been used to analyze social posts about tinnitus and analyze the heterogeneity of symptoms. Diagnostics for hearing problems, acoustics to facilitate hearing 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. Similarly, machine learning based versions of other auditory tests including determining dead regions in the cochlea or equal loudness contours, have been created. e-Research (remote testing, online experiments, new tools and frameworks) Examples of e-Research tools include including the Remote Testing Wiki, the Portable Automated Rapid Testing (PART), Ecological Momentary Assessment (EMA) and the NIOSH soundlevel meter. A number of tools can be found online.
Computational audiology
SoundBite Hearing System
Real ear measurement
Audio power amplifier
Vibroacoustic therapy
Conditioned play audiometry
Otoacoustic emission
Vibroacoustic stimulation