Parameter tuning of time-frequency masking algorithms for reverberant artifact removal within the cochlear implant stimulus.

Journal Article (Journal Article)

Cochlear implant recipients struggle to understand speech in reverberant environments. To restore speech perception, artifacts due to reverberant reflections can be removed from the cochlear implant stimulus by applying a matrix of gain values, a technique referred to as time-frequency masking . In this study, two common time-frequency masking strategies are implemented within cochlear implant processing, either introducing complete retention or deletion of stimulus components using a binary mask or continuous attenuation of stimulus components using a ratio mask. Parameters of each masking strategy control the level of attenuation imposed by the gain values. In this study, we perceptually tune the parameters of the masking strategy to determine a balance between speech retention and artifact removal. We measure the intelligibility of reverberant signals mitigated by each strategy with speech recognition testing in normal-hearing listeners using vocoding as a simulation of cochlear implant perception. For both masking strategies, we find parameterizations that maximize the intelligibility of the mitigated signals. At the best-performing parameterizations, binary-masked reverberant signals yield larger intelligibility improvements than ratio-masked signals. The results provide a perceptually optimized objective for the removal of reverberant artifacts from cochlear implant stimuli, facilitating improved speech recognition performance for cochlear implant recipients in reverberant environments.

Full Text

Duke Authors

Cited Authors

  • Shahidi, LK; Collins, LM; Mainsah, BO

Published Date

  • November 2022

Published In

Volume / Issue

  • 23 / 6

Start / End Page

  • 309 - 316

PubMed ID

  • 35875863

Pubmed Central ID

  • PMC9611765

Electronic International Standard Serial Number (EISSN)

  • 1754-7628

International Standard Serial Number (ISSN)

  • 1467-0100

Digital Object Identifier (DOI)

  • 10.1080/14670100.2022.2096182


  • eng