Effects of a transient noise reduction algorithm on speech intelligibility in noise, noise tolerance and perceived annoyance in cochlear implant users

Abstract

Objective: To evaluate the validity and efficacy of a transient noise reduction algorithm (TNR) in cochlear implant processing and the interaction of TNR with a continuous noise reduction algorithm (CNR). Design: We studied the effects of TNR and CNR on the perception of realistic sound samples with transients, using subjective ratings of annoyance, a speech-in-noise test and a noise tolerance test. Study sample: Participants were 16 experienced cochlear implant recipients wearing an Advanced Bionics Naida Q70 processor. Results: CI users rated sounds with transients as moderately annoying. Annoyance was slightly, but significantly reduced by TNR. Transients caused a large decrease in speech intelligibility in noise and a moderate decrease in noise tolerance, measured on the Acceptable Noise Level test. The TNR had no significant effect on noise tolerance or on speech intelligibility in noise. The combined application of TNR and CNR did not result in interactions. Conclusions: The TNR algorithm was effective in reducing annoyance from transient sounds, but was not able to prevent a decreasing effect of transients on speech understanding in noise and noise tolerance. TNR did not reduce the beneficial effect of CNR on speech intelligibility in noise, but no cumulated improvement was found either.

The Important Role of Contextual Information in Speech Perception in Cochlear Implant Users and Its Consequences in Speech Tests

“…The speech intelligibility and reading span data were available from three recent studies of our Department of Otorhinolaryngology: data of Vroegop, Dingemanse, van der Schroeff, Metselaar, and Goedegebure (2017) and data of and Dingemanse, Vroegop, and Goedegebure (2018). From Dingemanse and Goedegebure (2018), we included only 11 participants, because the other participants were already included from .…”

The relation of hearing-specific patient-reported outcome measures with speech perception measures and acceptable noise levels in cochlear implant users

“…(cf. Dingemanse and Goedegebure 2018; Dingemanse, Vroegop, and Goedegebure 2018;Vroegop et al 2017) In all test protocols, a practice run for the sentence-innoise test was performed to make the participants familiar with the voice and the task and to obtain a first estimation of a participants' SRTn. This practice run was followed by a sentence test in quiet, and a practice run for the ANL test to learn the procedure and to follow the instruction carefully.…”

Personalizing Transient Noise Reduction Algorithm Settings for Cochlear Implant Users

“…TNR bb-std was specifically designed to suppress transients with onset times in the order of 1 ms. It features a fast attack time and a high compression ratio to suppress high-intensity transients ( Dingemanse et al 2018 ).…”

“…Studies on the effectivity of TNR bb-std and related algorithms in CI users and listeners with HAs have shown mixed results. Most of them revealed a significant benefit on one or more subjective rating scales, for example, annoyance level of the transient noise, or overall sound comfort ( Keidser et al 2007 ; Liu et al 2012 ; Dingemanse et al 2018 ; Keshavarzi et al 2018 ), but failed to show a significant effect on speech intelligibility ( Keidser et al 2007 ; Dingemanse et al 2018 ). DiGiovanni et al (2011 ) reported no significant effects on any of the subjective ratings tested, but they did find a significant, yet marginal improvement of 4.4% of the speech intelligibility across various noise conditions.…”

“…The aim of our study was to investigate the merits of personalizing the attenuation and τ of two different TNR algorithms in an attempt to deliver personalized care for individual CI users. Personalization of TNR algorithms may hold promise, because the tolerance of CI users to noise transients varies substantially ( Dingemanse et al 2018 ). One possible reason for this observation is that above most comfortable stimulation (M) levels, the perceived loudness growth becomes unpredictable ( Fredelake et al 2014 ).…”