NDLI: An evolutionary optimization methodology of a low-power programmable Cochlear Implant

Content Provider	IEEE Xplore Digital Library
Author	Farago, P. Farago, C. Groza, R. Hintea, S.
Copyright Year	2015
Description	Author affiliation: Dept. of Bases of Electron., Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania (Farago, P.; Farago, C.; Groza, R.; Hintea, S.)
Abstract	Hearing loss is a common problem of the modern society, the compensation of which requires auditory prosthesis. Cochlear Implants (CI) are implantable electronic devices which create auditory sensations by directly stimulating the auditory neurons. Low-power operation and wide-range parameter programmability are key features of CIs. This article proposes an evolutionary approach to optimizing a CI sound processor to enhance speech understanding and auditory experience. The novelty of this work consists in having the auditory perception incorporated into the objective function of the optimization algorithm. Auditory perception is objectively estimated over a Matlab-Eldo simulation platform, which performs transistor-level simulation of a low-power CI sound processor to generate stimulus signals, followed by perceptive signal reconstruction using a first-order extracellular stimulation model. The optimization objective is to maximize the performance similarity measure between the original and the reconstructed auditory signals.
Starting Page	354
Ending Page	359
File Size	668498
Page Count	6
File Format	PDF
e-ISBN	9781479984985
DOI	10.1109/TSP.2015.7296283
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-09
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Band-pass filters Electrodes Perceptive signal reconstruction Cochlear implant low-power sound processor Neurons Auditory system Speech Simulation-based optimization platform Optimization Transconductance Genetic algorithms
Content Type	Text
Resource Type	Article

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