1. Suppressing reverberation in cochlear implant stimulus patterns using time-frequency masks based on phoneme groups.
- Author
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Kevin Chu, Collins, Leslie, and Mainsah, Boyla
- Subjects
COCHLEAR implants ,PHONEME (Linguistics) ,COMPUTER sound processing ,CLASSIFIERS (Linguistics) ,ACOUSTICS - Abstract
Cochlear implant (CI) users experience considerable difficulty in understanding speech in reverberant listening environments. This issue is commonly addressed with time-frequency masking, where a timefrequency decomposed reverberant signal is multiplied by a matrix of gain values to suppress reverberation. However, mask estimation is challenging in reverberant environments due to the large spectro-temporal variations in the speech signal. To overcome this variability, we previously developed a phoneme-based algorithm that selects a different mask estimation model based on the underlying phoneme. In the ideal case where knowledge of the phoneme was assumed, the phoneme-based approach provided larger benefits than a phoneme-independent approach when tested in normal-hearing listeners using an acoustic model of CI processing. The current work investigates the phoneme-based mask estimation algorithm in the real-time feasible case where the prediction from a phoneme classifier is used to select the phoneme-specific mask. To further ensure real-time feasibility, both the phoneme classifier and mask estimation algorithm use causal features extracted from within the CI processing framework. We conducted experiments in normal-hearing listeners using an acoustic model of CI processing, and the results showed that the phoneme-specific algorithm benefitted the majority of subjects. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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