Application of a Graphical Model to Investigate the Utility of Cross-channel Information for Mitigating Reverberation in Cochlear Implants.

Published

Conference Paper

Individuals with cochlear implants (CIs) experience more difficulty understanding speech in reverberant environ-ments than normal hearing listeners. As a result, recent research has targeted mitigating the effects of late reverberant signal reflections in CIs by using a machine learning approach to detect and delete affected segments in the CI stimulus pattern. Previous work has trained electrode-specific classification models to mitigate late reverberant signal reflections based on features extracted from only the acoustic activity within the electrode of interest. Since adjacent CI electrodes tend to be activated concurrently during speech, we hypothesized that incorporating additional information from the other electrode channels, termed cross-channel information, as features could improve classification performance. Cross-channel information extracted in real-world conditions will likely contain errors that will impact classification performance. To simulate extracting cross-channel information in realistic conditions, we developed a graphical model based on the Ising model to systematically introduce errors to specific types of cross-channel information. The Ising-like model allows us to add errors while maintaining the important geometric information contained in cross-channel information, which is due to the spectro-temporal structure of speech. Results suggest the potential utility of leveraging cross-channel information to improve the performance of the reverberation mitigation algorithm from the baseline channel-based features, even when the cross-channel information contains errors.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • December 2018

Published In

  • Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications

Volume / Issue

  • 2018 /

Start / End Page

  • 847 - 852

PubMed ID

  • 32016173

Pubmed Central ID

  • 32016173

Digital Object Identifier (DOI)

  • 10.1109/icmla.2018.00136