Machine learning and the auditory nerve
Keywords:Algorithms, Hearing aids, Neurology, Optimization, Parameter estimation, Problem solving, Signal encoding, Auditory nerves, Auditory systems, Neural Articulation Index (NAI), Neurocompensators, Speech Transmission Index (STI)
AbstractThe application of machine learning to the auditory system is studied. The application consists of four models such as model of the normal auditory system, impaired auditory system, processing block to train, and an error metric. The statistical differences between the normal and impaired auditory nerve reponses shows a loss of contrast between different auditory landmarks. It is expected that better segmentation will lead to more normal streaming, allowing the hearing-aid user the ability to unmask spectrally and temporally and also a normal hearing person.
How to Cite
Copyright on articles is held by the author(s). The corresponding author has the right to grant on behalf of all authors and does grant on behalf of all authors, a worldwide exclusive licence (or non-exclusive license for government employees) to the Publishers and its licensees in perpetuity, in all forms, formats and media (whether known now or created in the future)
i) to publish, reproduce, distribute, display and store the Contribution;
ii) to translate the Contribution into other languages, create adaptations, reprints, include within collections and create summaries, extracts and/or, abstracts of the Contribution;
iii) to exploit all subsidiary rights in the Contribution,
iv) to provide the inclusion of electronic links from the Contribution to third party material where-ever it may be located;
v) to licence any third party to do any or all of the above.