Cochlear implant simulator with independent representation of the full spiral ganglion

Grange, Jacques; Culling, John F.; Harris, Naomi S. L.; Bergfeld, Sven

doi:10.1121/1.5009602

Cited by 34 publications

(57 citation statements)

References 15 publications

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“…This indicates that the vocoder simulation with the more focussed current spread setting was successful in simulating the speech-reception performance of a group of CI subjects when listening to speech in babble and in conditions UN and PR. This extends the results of Grange et al (2017), who reported similar SRTs for CI subjects and NH subjects listening to stimuli processed with SPIRAL for speech in speech-shaped noise. However, it remains unknown if the SRTs would have been similar for simulated and real CI subjects for speech in traffic noise.…”

Section: Discussionsupporting

confidence: 88%

“…For the simulated current spread of À16 dB/oct, the average SRT for condition UN was 7.3 dB (ranging from 5.9 to 8.7 dB), which is consistent with SRTs obtained with well-performing CI users (e.g., 6.7 dB for the same 20T babble in Goehring et al, 2017;7.9 dB for a 4T babble in Croghan and Smith, 2018). Our SRTs are also consistent with those of Grange et al (2017), who reported that for speech-shaped noise a current spread setting of À16 dB/oct yielded SRTs with SPIRAL that matched those found for CI users.…”

Section: Discussionsupporting

confidence: 87%

“…SPIRAL decouples the analysis and carrier stages of the vocoder processing and combines a continuous mixture of envelopes from the analysis filters with a large number of carrier tones to simulate current spread and/or neural degeneration along the cochlea. It has been argued that the SPIRAL vocoder provides a more accurate simulation of the perceptual effects of current spread on speech perception than traditional noise-band or tone vocoders (Shannon et al, 1995;Oxenham and Kreft, 2014), and resulting speech scores more accurately match those obtained from CI listeners (Grange et al, 2017;Fletcher et al, 2018). We used 16 analysis filter bands within SPIRAL to represent the 16 electrode channels in CIs from Advanced Bionics (AB, Valencia, CA), and used two current decay slopes of À8 and À16 dB/oct to simulate the effects of current spread observed with typical CIs (Oxenham and Kreft, 2014).…”

Section: B CI Simulation and Listening Proceduresmentioning

confidence: 99%

“…Initially, two objective SI prediction methods were used to optimize and evaluate the RNN. The first experiment evaluated performance of the RNN for speech in babble using CI vocoder simulations presented to NH listeners (Oxenham and Kreft, 2014;Grange et al, 2017;Fletcher et al, 2018). Two simulated amounts of current spread were used to simulate CI users with electrodes positioned close to or far from the stimulated neural elements in an attempt to model the variability that characterizes the CI population, and evaluate its effects on the benefits of RNN processing over no processing.…”

Section: Introductionmentioning

confidence: 99%

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Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants

Goehring¹,

Keshavarzi²,

Carlyon³

et al. 2019

Preprint

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Speech-in-noise perception is a major problem for users of cochlear implants (CIs), especially with non-stationary background noise such as competing talkers or traffic. Algorithms that facilitate speech perception by attenuating background noise have produced benefits but relied on a priori information about the target speaker and/or background noise. We developed a recurrent neural network (RNN) algorithm for enhancing speech in non-stationary noise and evaluated its benefits for speech perception, using objective measures, experiments with normal-hearing (NH) subjects listening to CI simulations and experiments with CI users. The RNN was trained using a data set that included speech from many talkers mixed with a set of real-world multi-talker or traffic noise recordings. Its performance was evaluated using speech from a novel talker mixed with novel noise recordings of the same class, either babble or traffic noise. The signal-to-noise ratios also differed from those used for training. Objective measures indicated benefits of using a recurrent architecture over a simpler feed-forward architecture and predicted better speech intelligibility than for the unprocessed speech in noise. The experimental results showed significant improvements in speech intelligibility for the speech in babble for both CI simulations and CI subjects; speech reception thresholds were improved by 1.4 to 3.4 dB. There was no significant improvement for the traffic noise. CI subjects rated stimuli processed using the RNN algorithm as significantly better in terms of speech distortions, noise intrusiveness and overall quality than unprocessed stimuli for both babble and traffic noise, with larger improvements for the former. These results extend previous findings and indicate benefits in speech-in-noise performance by CI listeners for mostly unseen acoustic conditions when using a speaker-independent algorithm that was optimized for non-stationary noises.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 87%

Section: B CI Simulation and Listening Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants

Goehring¹,

Keshavarzi²,

Carlyon³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous studies have used normal-hearing (NH) listeners and noise-or tone-based envelope vocoder schemes to simulate the effects of current spread by varying the spectral overlap between channels and/or by changing the total number of channels (Qin and Oxenham, 2003;Fu and Nogaki 2005;Litvak et al, 2007;Bingabr et al 2008;Crew and Galvin, 2012;Oxenham and Kreft 2014;Mesnildrey and Macherey 2015;Grange et al 2017;Jahn et al, 2019). In those studies, spectral overlap between channels with slopes of about -12 dB/octave and about 4-8 channels led to similar results between speech-in-noise performances by NH listeners using vocoders and CI users.…”

Section: Introductionmentioning

confidence: 99%

Using spectral blurring to assess effects of channel interaction on speech-in-noise perception with cochlear implants

Goehring¹,

Arenberg²,

Carlyon³

2019

Preprint

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Cochlear implant (CI) listeners struggle to understand speech in background noise. Interactions between electrode channels due to current spread increase the masking of speech by noise and lead to difficulties with speech perception. Strategies that reduce channel interaction therefore have the potential to improve speech-in-noise perception by CI listeners, but previous results have been mixed. We investigated the effects of channel interaction on speech-in-noise perception and its association with spectro-temporal acuity in a listening study with 12 CI users. Instead of attempting to reduce channel interaction, we introduced spectral blurring to simulate some of the effects of channel interaction by adjusting the overlap between electrode channels at the input level of the analysis filters or at the output by using several simultaneously-stimulated electrodes per channel. We measured speech reception thresholds in noise as a function of the amount of blurring applied to either all 15 electrode channels or to 5 evenly spaced channels. Performance remained roughly constant as the amount of blurring applied to all channels increased up to some knee point, above which it deteriorated. This knee point differed across listeners in a way that correlated with performance on a non-speech spectro-temporal task, and is proposed here as an individual measure of channel interaction. Surprisingly, even extreme amounts of blurring applied to 5 channels did not affect performance. The effects on speech perception in noise were similar for blurring at the input and at the output of the CI. These results confirm the assumption that CI users make use of a limited number of effective channels, are highly resilient against spectral distortions and can tolerate substantial deviations from their everyday settings without harming their speech perception. These findings may explain the mixed results by strategies that optimized or deactivated a small number of electrodes evenly distributed along the array.

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Speech Perception with Spectrally Non-overlapping Maskers as Measure of Spectral Resolution in Cochlear Implant Users

O’Neill

Kreft

Oxenham

2018

JARO

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Poor spectral resolution contributes to the difficulties experienced by cochlear implant (CI) users when listening to speech in noise. However, correlations between measures of spectral resolution and speech perception in noise have not always been found to be robust. It may be that the relationship between spectral resolution and speech perception in noise becomes clearer in conditions where the speech and noise are not spectrally matched, so that improved spectral resolution can assist in separating the speech from the masker. To test this prediction, speech intelligibility was measured with noise or tone maskers that were presented either in the same spectral channels as the speech or in interleaved spectral channels. Spectral resolution was estimated via a spectral ripple discrimination task. Results from vocoder simulations in normal-hearing listeners showed increasing differences in speech intelligibility between spectrally overlapped and interleaved maskers as well as improved spectral ripple discrimination with increasing spectral resolution. However, no clear differences were observed in CI users between performance with spectrally interleaved and overlapped maskers, or between tone and noise maskers. The results suggest that spectral resolution in current CIs is too poor to take advantage of the spectral separation produced by spectrally interleaved speech and maskers. Overall, the spectrally interleaved and tonal maskers produce a much larger difference in performance between normal-hearing listeners and CI users than do traditional speech-in-noise measures, and thus provide a more sensitive test of speech perception abilities for current and future implantable devices.

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Cochlear implant simulator with independent representation of the full spiral ganglion

Cited by 34 publications

References 15 publications

Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants

Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants

Using spectral blurring to assess effects of channel interaction on speech-in-noise perception with cochlear implants

Speech Perception with Spectrally Non-overlapping Maskers as Measure of Spectral Resolution in Cochlear Implant Users

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