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

Abstract

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.

Key Words:

• Cochlear Implants
• Reverberation
• Time-frequency masking
• ACE processing

Acknowledgements

The authors would like to thank the subjects who participated in this experiment.

Disclaimer statements

Contributors None.

Conflicts of interest None.

Ethics approval This study was approved by the Duke University Institutional Review Board.

Supplemental data

Supplemental data for this article can be accessed at https://doi.org/10.1080/14670100.2022.2096182.

Additional information

Funding

This work was funded by the National Institutes of Health via a grant administered by the National Institute on Deafness and Other Communication Disorders (R01-DC014290).

Notes on contributors

Lidea K. Shahidi

Lidea K. Shahidi received her PhD in Electrical and Computer Engineering from Duke University in May 2022. She is currently a Research Associate at the University of Cambridge working in the Cambridge Hearing Group. Her research interests include speech enhancement and speech coding strategies for cochlear implants.

Leslie M. Collins

Leslie M. Collins (PhD) is a Professor of Electrical and Computer Engineering at Duke University.

Boyla O. Mainsah

Boyla O. Mainsah (PhD) is an Assistant Research Professor of Electrical and Computer Engineering at Duke University.