Machine learning and the auditory nerve

Jeff Bondy, Ian C. Bruce, Sue Becker, Simon Haykin

Abstract


The 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.

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)

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