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
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.
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