Transient Noise Reduction in Cochlear Implant Users: A Multi-Band Approach

Dyballa, Karl-Heinz; Hehrmann, Phillipp; Hamacher, Volkmar; Lenarz, Thomas; Buechner, Andreas

doi:10.4081/audiores.2016.154

Cited by 5 publications

(18 citation statements)

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“…The MCTR algorithm used here differs from most transient-reduction algorithms 373 described in the literature (with the exception of Dyballa et al, 2016), in that transients are 374 detected and attenuated in a frequency-selective manner. Thus, attenuation of transients 375 dominated by high frequencies did not affect the gain applied to low frequencies, and vice versa.…”

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confidence: 99%

Evaluation of a multi-channel algorithm for reducing transient sounds

Keshavarzi

Baer

Moore

2018

International Journal of Audiology

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confidence: 99%

Evaluation of a multi-channel algorithm for reducing transient sounds

Keshavarzi

Baer

Moore

2018

International Journal of Audiology

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“…The noise type used was a “cafeteria” noise, consisting of a mix of clattering dishes, and reverberant multi-talker babble. The noise was identical to the one described previously ( Dyballa et al 2016 ). To the normal-hearing ear, the noise was acoustically perceived as a loud clatter with a damped murmur in the background.…”

Section: Methodsmentioning

confidence: 99%

“…TNR mb divides the input audio signal into four frequency bands (0 to 1000, 1000 to 2000, 2000 to 4000, and >4000) and independently applies the detection and attenuation to these bands using the same detector as TNR bb-std . The attenuations are proportional to the band-specific transient amplitudes and vary from 10 to 30 dB ( Dyballa et al 2016 ). The authors reported significantly better speech intelligibility with TNR mb-std in transient noise than with the TNR switched off (TNR off ), and it significantly outperformed TNR bb-std .…”

Section: Introductionmentioning

confidence: 99%

“…CI users generally experience difficulty understanding speech even in positive signal-to-noise ratios (SNRs), while HA users perform better and can tolerate even negative SNRs. Given that TNRs expectedly more effectively detect and suppress transients at low SNRs ( Dyballa et al 2016 ), they may be less beneficial for CI users than for people with a HA.…”

Section: Introductionmentioning

confidence: 99%

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Personalizing Transient Noise Reduction Algorithm Settings for Cochlear Implant Users

et al. 2021

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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...

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“…Finally, if both the attack and release time are made very short, so as to reduce the gain for intense transient sounds, this can lead to reduced overall sound quality (Tan & Moore, 2004). Several transient noise reduction (TNR) algorithms have been developed to mitigate these problems (Digiovanni et al, 2011;Dingemanse et al, 2018;Dyballa et al, 2015Dyballa et al, , 2016Hirszhorn et al, 2012;Keshavarzi, Baer, et al, 2018;Korhonen et al, 2013). Digiovanni et al (2011) studied the effects of two different TNR algorithms on speech intelligibility and subjective ratings of sound comfort, sound quality, and speech understanding for hearing-impaired (HI) participants.…”

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Transient Noise Reduction Using a Deep Recurrent Neural Network: Effects on Subjective Speech Intelligibility and Listening Comfort

2021

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A deep recurrent neural network (RNN) for reducing transient sounds was developed and its effects on subjective speech intelligibility and listening comfort were investigated. The RNN was trained using sentences spoken with different accents and corrupted by transient sounds, using the clean speech as the target. It was tested using sentences spoken by unseen talkers and corrupted by unseen transient sounds. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective speech intelligibility and listening comfort for two relative levels of the transients. The conditions were: no processing (NP); processing using the RNN; and processing using a multi-channel transient reduction method (MCTR). Ten participants with normal hearing and ten with mild-to-moderate hearing loss participated. For the latter, frequency-dependent linear amplification was applied to all stimuli to compensate for individual audibility losses. For the normal-hearing participants, processing using the RNN was significantly preferred over that for NP for subjective intelligibility and comfort, processing using the RNN was significantly preferred over that for MCTR for subjective intelligibility, and processing using the MCTR was significantly preferred over that for NP for comfort for the higher transient level only. For the hearing-impaired participants, processing using the RNN was significantly preferred over that for NP for both subjective intelligibility and comfort, processing using the RNN was significantly preferred over that for MCTR for comfort, and processing using the MCTR was significantly preferred over that for NP for comfort.

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Transient Noise Reduction in Cochlear Implant Users: A Multi-Band Approach

Cited by 5 publications

References 15 publications

Evaluation of a multi-channel algorithm for reducing transient sounds

Evaluation of a multi-channel algorithm for reducing transient sounds

Personalizing Transient Noise Reduction Algorithm Settings for Cochlear Implant Users

Transient Noise Reduction Using a Deep Recurrent Neural Network: Effects on Subjective Speech Intelligibility and Listening Comfort

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