Related Experiment Video
Updated: Apr 19, 2026
Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
Published on: March 24, 2023
Application of Noise Reduction Algorithm ClearVoice in Cochlear Implant Processing: Effects on Noise Tolerance and
J Gertjan Dingemanse1, André Goedegebure
1Department of ENT, Erasmus Medical Center, Rotterdam, The Netherlands.
Objectives:
Noise reduction algorithms have recently been introduced in the design of clinically available cochlear implants. This study was intended to (1) evaluate the effect of noise reduction algorithm "ClearVoice" on noise tolerance and on speech intelligibility in noisy conditions at different speech-in-noise ratios in cochlear implant users, and (2) test the hypothesis that CI recipients with low spectral resolution might benefit more from noise reduction algorithms than CI users with high spectral resolution.
Design:
A double-blind crossover design was used to measure the effect of the noise reduction algorithm ClearVoice on noise tolerance with the acceptable noise level (ANL) test and on speech in noise for three performance levels: speech reception thresholds (SRT) at 50%, 70%, and at a speech to noise ratio of SRT50% + 11 dB. Furthermore, they tested speech intelligibility in quiet. The effective spectral resolution was measured with a spectral-ripple discrimination test. Twenty users of the Advanced Bionics Harmony processor with HiRes120-processing participated in this study.
Results:
The noise reduction algorithm led to a significant improvement-a decrease of 3.6 dB-in the ANL test but had no significant effect on any of the three speech-in-noise performance levels. The improvement in ANL was not significantly correlated with any of the speech-in-noise measures, nor with the speech-in-noise ratio in the ANL test. However, higher maximum speech intelligibility in quiet conditions correlated significantly with higher noise tolerance. Spectral-ripple discrimination thresholds were not significantly correlated with the effect of noise reduction on ANL or on speech intelligibility in noise nor with the speech-in-noise ratios. The spectral-ripple discrimination thresholds did correlate significantly with maximum speech intelligibility in quiet but not with speech reception thresholds in noise.
Conclusions:
The noise reduction algorithm ClearVoice improves noise tolerance. However, this study shows no change in speech intelligibility in noise due to the algorithm. The improvement in noise tolerance is not significantly related to spectral-ripple discrimination thresholds, speech intelligibility measures, or signal to noise ratio. Our hypothesis that CI recipients with low spectral resolution have a greater benefit from noise reduction than CI users with high spectral resolution does not hold for noise tolerance or for speech intelligibility in noise.
