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A Novel Model-Based Hearing Compensation Design Using a Gradient-Free Optimization Method

zhechen{at}soma.ece.mcmaster.ca, Department of Electrical and Computer Engineering, McMaster University Hamilton, Ontario L85 4k1, Canada

Suzanna Becker

becker{at}mcmaster.ca, Department of Psychology, McMaster University Hamilton, Ontario L85 4k1, Canada

Jeff Bondy

jeff{at}soma.ece.mcmaster.ca, Department of Electrical and Computer Engineering, McMaster University Hamilton, Ontario L85 4k1, Canada

Ian C. Bruce

ibruce{at}ieee.org, Department of Electrical and Computer Engineering, McMaster University Hamilton, Ontario L85 4k1, Canada

Simon Haykin

haykin{at}mcmaster.ca, Department of Electrical and Computer Engineering, McMaster University Hamilton, Ontario L85 4k1, Canada

We propose a novel model-based hearing compensation strategy and gradient-free optimization procedure for a learning-based hearing aid design. Motivated by physiological data and normal and impaired auditory nerve models, a hearing compensation strategy is cast as a neural coding problem, and a Neurocompensator is designed to compensate for the hearing loss and enhance the speech. With the goal of learning the Neurocompensator parameters, we use a gradient-free optimization procedure, an improved version of the ALOPEX that we have developed (Haykin, Chen, & Becker, 2004), to learn the unknown parameters of the Neurocompensator. We present our methodology, learning procedure, and experimental results in detail; discussion is also given regarding the unsupervised learning and optimization methods.