Personalizing Transient Noise Reduction Algorithm Settings for Cochlear Implant Users

Abstract

Objectives: Speech understanding in noise is difficult for patients with a cochlear implant. One common and disruptive type of noise is transient noise. We have tested transient noise reduction (TNR) algorithms in cochlear implant users to investigate the merits of personalizing the noise reduction settings based on a subject’s own preference. Design: The effect of personalizing two parameters of a broadband and a multiband TNR algorithm (TNR bb and TNR mb , respectively) on speech recognition was tested in a group of 15 unilaterally implanted subjects in cafeteria noise. The noise consisted of a combination of clattering dishes and babble noise. Each participant could individually vary two parameters, namely the scaling factor of the attenuation and the release time (τ). The parameter τ represents the duration of the attenuation applied after a transient is detected. As a reference, the current clinical standard TNR “SoundRelax” from Advanced Bionics was tested (TNR bb-std ). Effectiveness of the algorithms on speech recognition was evaluated adaptively by determining the speech reception threshold (SRT). Possible subjective benefits of the algorithms were assessed using a rating task at a fixed signal-to-noise ratio (SNR) of SRT + 3 dB. Rating was performed on four items, namely speech intelligibility, speech naturalness, listening effort, and annoyance of the noise. Word correct scores were determined at these fixed speech levels as well. Results: The personalized TNR mb improved the SRT statistically significantly with 1.3 dB, while the personalized TNR bb degraded it significantly by 1.7 dB. For TNR mb , we attempted to further optimize its settings by determining a group-based setting, leaving out those subjects that did not experience a benefit from it. Using these group-based settings, however, TNR mb did not have a significant effect on the SRT any longer. TNR bb-std did not affect speech recognition significantly. No significant effects on subjective ratings were found for any of the items investigated. In addition, at a constant speech level of SRT + 3 dB, no effect of any of the algorithms was found on word correct scores, including TNR mb with personalized settings. Conclusions: Our study results indicate that personalizing noise reduction settings of a multiband TNR algorithm can significantly improve speech intelligibility in transient noise, but only under challenging listening conditions around the SRT. At more favorable SNRs (SRT + 3 dB), this benefit was lost. We hypothesize that TNR mb was beneficial at lower SNRs, because of more effective artifact detection under those conditions. Group-averaged settings of the multiband al...

Beamforming and Single-Microphone Noise Reduction: Effects on Signal-to-Noise Ratio and Speech Recognition of Bimodal Cochlear Implant Users

“…Because SRT omni is present in both factors, the terms are mathematically coupled. To uncouple the data, the effect of SNRA was correlated to the average SRT (SRT SNRA + SRT omni )/2 instead ( Carlyon et al, 2018 ; Stronks et al, 2021 ). Only SRTs obtained in LTSS noise and omnidirectional microphone settings (i.e., no beamforming) were used.…”

The Benefit of Bimodal Hearing and Beamforming for Cochlear Implant Users

“…Because both the abscissa and ordinate contained an identical factor/element (i.e., SRT BetterEar ), the data were mathematically coupled, which artificially introduces a correlation [22]. To decouple the data [23], we correlated the binaural benefit to the magnitude of the SRT difference between the ears with BB switched on (abs(SRT HA+BB − SRT CI+BB )).…”