Using computational auditory models to predict simultaneous masking data: model comparison.

Published

Journal Article

In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology. One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates [15], [1]. To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks [20]. Previous efforts, including our own recent work using the Auditory Image Model, have demonstrated the validity of this type of analysis; however, theoretical predictions often continue to exceed experimentally-measured performance [9], [21]. In this paper, we compare predictions of detection performance across several computational auditory models. We also reconcile some of the previously observed discrepancies by incorporating appropriate signal uncertainty into the optimal detector.

Full Text

Duke Authors

Cited Authors

  • Huettel, LG; Collins, LM

Published Date

  • December 1999

Published In

Volume / Issue

  • 46 / 12

Start / End Page

  • 1432 - 1440

PubMed ID

  • 10612901

Pubmed Central ID

  • 10612901

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

International Standard Serial Number (ISSN)

  • 0018-9294

Digital Object Identifier (DOI)

  • 10.1109/10.804571

Language

  • eng