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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/41261

Title: Evaluating Sensorineural Hearing Loss With An Auditory Nerve Model Using A Mean Structural Similarity Measure.
Author: HARTE, NAOMI
HINES, ANDREW
Author's Homepage: http://people.tcd.ie/nharte
Keywords: Biomaterials
Hearing loss
Issue Date: 2010
Citation: Andrew Hines and Naomi Harte, Evaluating Sensorineural Hearing Loss With An Auditory Nerve Model Using A Mean Structural Similarity Measure., European Signal Processing Conference (EUSIPCO '10)., Aalborg, Denmark, 2010
Abstract: Hearing loss research has traditionally been based on perceptual criteria, speech intelligibility and threshold levels. The development of computational models of the auditory-periphery has allowed experimentation via simulation to provide quantitative, repeatable results at a more granular level than would be practical with clinical research on human subjects. This work seeks to create an objective measure to automate this inspection process and ranks hearing losses based on auditory-nerve discharge patterns. A systematic way of assessing phonemic degradation using the outputs of an auditory nerve model for a range of sensorineural hearing losses would aid in rapid prototyping development of speech-processing algorithms for digital hearing aids. The effect of sensorineural hearing loss (SNHL) on phonemic structure was evaluated in this study using two types of neurograms: temporal fine structure (TFS) and average discharge rate or temporal envelope. The mean structural similarity index (MSSIM) is an objective measure originally developed to assess perceptual image quality. The measure is adapted here for use in measuring the phonemic degradation in neurograms derived from impaired auditory nerve outputs. A full evaluation of the choice of parameters for the metric is presented using a large amount of natural human speech. The metric’s boundedness and the results for TFS neurograms indicate it is a superior metric to standard point to point metrics of relative mean absolute error and relative mean squared error.
Description: PUBLISHED
URI: http://hdl.handle.net/2262/41261
Appears in Collections:Electronic & Electrical Eng (Scholarly Publications)

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