Forward-Masked Frequency Selectivity Improvements in Simulated and Actual Cochlear Implant Users Using a Preprocessing Algorithm

Abstract

Frequency selectivity can be quantified using masking paradigms, such as psychophysical tuning curves (PTCs). Normal-hearing (NH) listeners show sharp PTCs that are level- and frequency-dependent, whereas frequency selectivity is strongly reduced in cochlear implant (CI) users. This study aims at (a) assessing individual shapes of PTCs in CI users, (b) comparing these shapes to those of simulated CI listeners (NH listeners hearing through a CI simulation), and (c) increasing the sharpness of PTCs using a biologically inspired dynamic compression algorithm, BioAid, which has been shown to sharpen the PTC shape in hearing-impaired listeners. A three-alternative-forced-choice forward-masking technique was used to assess PTCs in 8 CI users (with their own speech processor) and 11 NH listeners (with and without listening through a vocoder to simulate electric hearing). CI users showed flat PTCs with large interindividual variability in shape, whereas simulated CI listeners had PTCs of the same average flatness, but more homogeneous shapes across listeners. The algorithm BioAid 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, particularly in seven out of eight CI users, meaning significantly sharper PTCs than in the unprocessed condition. The results indicate that algorithms can improve the large-scale sharpness of frequency selectivity in some CI users. This finding may be useful for the design of sound coding strategies particularly for situations in which high frequency selectivity is desired, such as for music perception.