Frequency selectivity improvements in individual cochlear implant users with a biologically-inspired preprocessing algorithm
Abstract
The ability to distinguish between two sounds of different frequency is known as frequency selectivity, which can be quantified using psychoacoustic tuning curves (PTCs). Normal-hearing (NH) listeners show level- and frequency-dependent sharp PTCs, whereas frequency selectivity is strongly reduced in cochlear implant (CI) users. This study aims at (i) assessing the individual shapes of PTCs measured psycho-acoustically in CI users, (ii) comparing these shapes to those of simulated CI listeners, and (iii) improving the sharpness of PTCs using a biologically-inspired preprocessing algorithm. A 3-alternative-forced-choice forward masking technique was used to assess PTCs in eight CI users (with their own speech processor) and 11 NH listeners (with and without listening to a vocoder to simulate electric hearing). CI users showed large inter-individual variability in sharpness, whereas simulated CI listeners had shallow, but homogeneous PTCs. Furthermore, a biologically-inspired dynamic compression algorithm was used to process the stimuli before entering the CI users’ speech processor or the vocoder simulation. This algorithm was able to partially restore frequency selectivity in both groups, meaning significantly sharper PTCs than unprocessed.
References
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