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Although other modelling methodologies (i.e., the WKB-numeric method) have been successfully employed in modelling the cochlea, this paper will focus on FEM, as it is more intuitive to clinicians [ 3, 4].
Another objective of modelling the cochlea is to predict phenomena yet to be observed.
Assumptions and data fitting are always used in modelling the cochlea, especially for the models of cochlear micromechanics.
The motivations of modelling the cochlea are to represent, within one framework, the results from a large variety of experiments and to explain the functions of the hearing system.
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A two-dimensional channel having a height to length ratio equal to ε is used to model the cochlea.
Finite element method (FEM) is one of the most popular discrete mathematical modelling techniques, mainly used in engineering that has been increasingly used to model the cochlea and its elements.
Besides replicating experimental finding in laboratory animals, the other important goal of modelling the mammalian (human) cochlea is to predict hearing defects [ 238, 239] or the effects of cochlear implants.
There he designed a chip that modeled the human cochlea, the snail-like organ in the inner ear that converts vibrations into electrical signals that are perceived by the brain as sound.
The idea of modelling the active function in the cochlea was developed in the 1970s and early 1980s and comprises two basic aspects: (1) the normal cochlear function depends on an active, mechanical feedback processes, and (2) OHCs operate as the agent of feedback.
At the first stage, which models the cochlea, the audio signal is converted into an auditory representation by employing the constant-Q transform (CQT .The CQT is a time-frequency representation, where the frequency bins are geometrically spaced and the Q-factors (i.e., the ratios of the center frequencies to the bandwidths) of all bins are equal [44].
In this model the cochlea is divided into 350 sections and each section is modelled using a small number of lumped elements.
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modelling the growth
modelling the variability
modelling the number
modelling the likelihood
modelling the source
modelling the distribution
modelling the paper
modelling the variance
modelling the system
modelling the disease
modelling the energy
modelling the spread
modelling the response
modelling the performance
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