Format

Send to

Choose Destination
Neural Comput. 2005 Dec;17(12):2648-71.

A novel model-based hearing compensation design using a gradient-free optimization method.

Author information

1
Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario L85 4k1, Canada. zhechen@soma.ece.mcmaster.ca

Abstract

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, 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.

PMID:
16212766
DOI:
10.1162/089976605774320575
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Atypon
Loading ...
Support Center