PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Spectral Blurring in Cochlear Implants: Association with Channel
Interaction and Effects on Speech-in-Noise Perception
Tobias GOEHRING1; Julie G. ARENBERG2; Robert P. CARLYON1
1

MRC Cognition and Brain Sciences Unit, University of Cambridge, UK2

Massachusetts Eye and Ear, Harvard Medical School, US

ABSTRACT
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 reduce the effective
number of channels a CI provides. Therefore, strategies to reduce channel interaction have the potential to
improve speech-in-noise perception by CI listeners. 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. By adjusting the spectral overlap in terms of acoustic bandwidths between electrode channels (spectral
blurring), we simulated some of the effects of channel interaction and measured speech reception thresholds
in noise as a function of the amount of blurring applied to either all, or 5 out of 15, electrode channels.
Performance for each listener 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 correlated with performanceon a non-speech spectro-temporal task. Surprisingly, even extreme amounts of blurring applied to 5 channels
did not affect performance overall. Findings show the resilience of CI listeners against spectral blurring andillustrate the difficulties faced by optimization strategies.
Keywords: Cochlear implants, Channel interaction, Speech perception

1. INTRODUCTION
Cochlear implants (CIs) restore hearing to deaf people by stimulating the auditory nerve with anarray of electrodes. While CI listeners achieve good speech understanding in quiet acoustic conditions,most of them struggle to understand speech in noise (1, 2). Efforts to improve the perception of speechcorrupted by background sounds by applying noise reduction techniques have provided benefits insome conditions but struggle to provide consistent benefits in the most challenging conditions withcompeting talkers (3, 4, 5). The strong limitations in speech-in-noise perception by CI users are likely
due to interactions between the different electrodes, each of which is used to convey information about
a different frequency region of the incoming sound (6).
Previous studies tried to alleviate this limitation by more precisely focussing the current provided
by each electrode (7, 8), or by deactivation of a subset of electrodes (8, 9, 10, 11). Studies using thedeactivation of channels (“site-selection”) were motivated by the assumption that the selective
stimulation with a subset of “good” electrode channels, as defined by direct or proxy measures ofelectrode-to-nerve distance, local neural health or spread of excitation, improves speech perception
over using all or a subset of “bad” electrode channels. Studies using current focusing techniques (“siteenhancement”) were motivated by the assumption that a more spatially-restricted neural excitation
profile decreases channel interaction and therefore improves speech-in-noise perception. However,results were mixed with some studies reporting improvements in speech -in-noise perception at grouplevel while others did not. Furthermore, methodologies and experimental designs differed between
studies and there were additional limitations, for example the use of acute testing versus providing
longer periods of acclimatization to the experimental settings, that made the interpretation of results
and comparisons between studies difficult.
1

Tobias.Goehring@mrc-cbu.cam.ac.uk

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The extent to which channel interactions affect speech-in-noise perception in CI users, and how
strongly this relationship is affected by the contributions of individual electrode channels, remain
unclear. It seems logical that the assumption of a potential improvement in speech-in-noise
performance due to a decrease in channel interaction also applies for the opposite direction; that anincrease in channel interaction will result in a deterioration of speech -in-noise performance. Here, we
test this assumption and aim to quantify the degree of channel interaction necessary for impairing
speech-in-noise performance in CI users. We speculate that the effects observed with the testing of
this inverted assumption can provide information on the degree to which channel interaction alters
speech-in-noise performance in CI users per se, and can further be used to estimate the amount ofchannel interaction at an individual level. We argue that these results will be useful to inform CI
optimization strategies and can potentially help to interpret previous results.
Here, we altered channel interaction in CI users by changing the spectral overlap between either
all or one-third of the electrode sites used for stimulation (“spectral blurring”) and measured effects
on speech-in-noise perception. The main hypothesis was that increasing amounts of spectral blurring
degrade speech-in-noise performance at group level. The second hypothesis under test was that there
is a negative relationship between the effects of spectral blurring on speech-in-noise performance andspectro-temporal resolution at an individual level, such that CI listeners with a poorer spectrotemporal acuity, as measured with the spectro-temporal test STRIPES (12), will be affected only bylarger amounts of blurring and vice versa.

2. METHODS
2.1 Subjects
Twelve post- and peri-lingually deafened, native speakers of British English took part. Half ofthem were female and their mean age was 67 years, with a range from 49 to 76 years. Subjects were
unilaterally implanted users of an Advanced Bionics (“AB”; Valencia, CA, USA) HiRes 90K TM
cochlear implant and had at least 3 years of experience with their device with a mean duration of
implant use of 5.8 years. Only the implanted ear of each subject was used for the presentation of
stimuli. If a subject was wearing a hearing aid in the other ear, then it was taken off during theexperiment. Prior to the experiment, the most recent clinical MAP was obtained for each subject.
Details about the demographic information and devices used by the subjects are given in Table 1.
The study was approved by the National Research Ethics committee for the East of England.
Subjects gave their informed consent and were paid for taking part and reimbursed for travel expenses.

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electrode

AB3

M

72

11

36

HR90K Naida

HiFocus 1J

S2

AB1

M

74

10

41

HR90K Harmony

HiFocus 1J

S3

AB6

F

70

5

65

HR90K Naida

HiFocus 1J

S4

AB24

F

49

3

4

HR90K Advantage

HiFocus MS

S5

AB26

F

58

4

21

HR90K Advantage

HiFocus MS

S6

AB23

F

60

3

58

HR90K Advantage

HiFocus MS

S7

AB25

F

66

3

34

HR90K Advantage

HiFocus MS

S8

AB2

F

60

11

27

HR90K

HiFocus 1J

S9

AB20

M

73

3

40

HR90K Naida

HiFocus MS

S10

AB05

M

76

9

27

HR90K Harmony

HiFocus 1J

S11

AB19

M

75

3

-

HR90K Naida

HiFocus MS

S12

AB09

M

73

5

-

HR90K Naida

HiFocus MS

array

S1

CI

Age (y)processor

Sex

CI speechof deafness

SpecifierDuration

Subject

Duration

implanted

Table 1 – Subject demographics and devices

2.2 Spectral blurring
Interaction effects between adjacent electrode channels are supposedly the most limiting factor forspeech-in-noise perception with CIs. We varied the amount of spectral blurring, as a means to alterchannel interaction, by adjusting the spectral overlap between electrode channels in terms of theacoustic bandwidths of the input analysis filters. The Advanced Bionics (AB) CI speech processor
makes use of a 16-band analysis filter bank with a frequency range from 238 up to 8054 Hz. The filterbank channels are constructed by combining sets of output bins obtained from an FFT analysis stage
and do not normally overlap with adjacent channels in the standard clinical MAP. Using the standard
clinical MAP for each subject as starting point, we generated a set of experimental MAPs per subject
by changing the lower and upper cut-off frequencies of the individual filter bank channels (using
BEPS+ software from AB), therefore de- or increasing the spectral overlap between adjacent channels.
The center frequencies of the filter channels were kept constant between all MAPs, and only the
bandwidth of each “blurred” channel was multiplied by a factor of 0.5, 2, 3, 4, 6 or 8. This led, forexample in the case of a blurring factor of 8, to filter bandwidths that were 8-fold wider than in thestandard clinical MAP. We compensated for the wider bandwidths of the filter channels by applying acorrection gain to equate for loudness. Six different blurring factors were used to generate 12
experimental MAPs in total. Half of those were generated by applying the spectral blurring to all 15
active electrode channels (ALL) and half were generated by blurring 5-of-15 active electrode channels(5-of-15) that were distributed equally along the array (electrodes 2,5,8,11 and 14). In the following,
the MAP similar to the clinical MAP is noted as “M1” and the experimental MAPs are noted as “M05,
M2, M3, …”, for the ALL condition, and “M05b, M2b, M3b, …” for the 5-of-15 condition. It should
be noted that electrode 16 was deactivated for all subjects in this experiment. Furthermore, theprocessing strategy was changed to HiRes-S for all subjects (comparable to continuous interleaved
sampling, CIS, without any current steering or noise reduction functions a ctive).
2.3 Speech-in-noise test
Speech-in-noise (SIN) performance was tested using sentence lists from the BKB corpus (13)spoken by a British male talker and mixed with time-reversed speech from the Harvard sentences
spoken by a different British male talker. This background noise contained the highly-modulatedcharacteristics of competing speech, as it occurs in realistic listening environments, but avoided
informational masking with the use of an unintelligible masker (14). We used an adaptive one-up/onedown procedure (15) to measure the speech reception threshold (SRT50) at which 50% of thesentences were understood correctly. The initial signal-to-noise ratio (SNR) was set to 4 dB SNR, andincreased by 2 dB per trial, while repeating a randomly-drawn sentence from the list, until the subject
recognized the three keywords. The adaptive procedure adjusted the SNR with a step size of 2 dB
until all 15 sentences of that list had been presented. A trial was deemed correct if all three keywords
were correctly repeated by the subject and the final SRT score for that run was calculated as theaverage of the last ten SNRs presented.
2.4 Spectro-temporal test
The Spectro-Temporal Ripple for Investigating Processor EffectivenesS (STRIPES, 12) test uses
an adaptive procedure to measure the threshold at which the subject can just distinguish the target
stimulus from two reference stimuli in a three-interval, two-alternative forced-choice task. Stimuli
consisted of 1s-long, concurrent exponential sine sweeps moving up or down in frequency from 250
to 8000 Hz. The subject had to select the target interval, which was either the first or last interval, andwhich was always an upward sweep; the other two intervals contained downward sweeps. The numberof concurrent frequency sweeps (the “density”) is varied to titrate difficulty, with the task being very
easy at a density close to 1, and progressively harder at higher densities. The starting frequency w as
roved across trials and the beginning and end of each interval was masked by short noise bursts to
reduce the salience of onset and offset cues. An adaptive two-up/one-down procedure started with asweep density of 1.1 (number of sweeps concurrently presented during each sweep) and adjusted thedensity per trial with a density step size of 0.5 (for the first 4 reversals) and 0.2 (for the last eig ht
reversals). The test was complete after 12 reversals and the final score of the run was calculated as
the average of the last four reversals.

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2.5 Experimental procedure
The experiment took place in a sound-attenuated testing room. The experiment was performed witha programmable Harmony CI speech processor (Advanced Bionics, US) that was worn by the subjects
during the testing. Stimuli were generated in MATLAB (Mathworks, US) using a battery-powered
laptop computer (Dell XPS15, Windows 10 Pro) that was connected via an external soundcard (Roland
UA-55) and an audio cable to the auxiliary input of the CI speech processor. The presentation level
was set for clean speech stimuli by adjusting the manual volume control of the soundcard to a“comfortable level” for each subject (level 6 on the loudness scale provided by Advanced Bionics).
The SIN stimuli were calibrated to the same RMS level as the stimuli used to set the presentation level
which was kept constant for all MAPs under test. In addition, subjects were asked during the testing
if the presentation level was comfortable to them for each of the different MAPs and this was
confirmed by all subjects and for all MAPs.
The experiment was split into two 3-hour sessions per subject that were performed on two different
days. In the first session, subjects completed the SIN test firstly with five MAPs (M05, M1, M2, M3,
M4) in random order from the ALL condition and secondly with five MAPs (M05b, M1b, M2b, M3b,M4b) in random order from the 5-of-15 condition. For each MAP and before the SIN test, subjects
were presented with one list (10 sentences) of a randomly chosen list from the Harvard sentences andwere able to read along to acclimatize to that MAP. The SIN test was then performed twice per MAP
and the average of the two runs was taken as the final SRT for that MAP. In the second session,
subjects were tested with three MAPs (M4, M6, M8) in random order from the ALL conditions andthen with three MAPs (M4b, M6b, M8b) in random order from the 5 -of-15 condition. The sameprocedure as in the first session was followed. After the SIN testing was complete, subjects performed
three runs of the STRIPES test with M1 and the average was taken as their final STRIPES score .

3. RESULTS
3.1 Speech-in-noise scores at group level
Group average scores for the SIN test are shown in Fig. 1 for the ALL condition and the 5-of-15
condition. We compared performance across conditions using one-way repeated-measures ANOVAs
with the factor MAP. For the ALL condition, there was a significant main effect of MAP [ F(6,66) =19.68, p < 0.001]. Pairwise comparisons with Bonferroni correction revealed significant differences
between M8 and all other MAPs (p < 0.022) and there were two comparisons, M05 vs. M6 (p = 0.059)
and M05 vs. M4 (p = 0.061), that just missed significance. For the 5-of-15 condition, there was no
significant main effect of MAP [F(6,66) = 0.72, p = 0.634]. Average performance across MAPs was
significantly better for the 5-of-15 condition than for the ALL condition as indicated by a paired t-test(t(6) = 4.04, p = 0.007), with a mean difference in SRT of 3.5 dB.

Figure 1 – Group SRT scores for all MAPs for the conditions with all active electrodes blurred (ALL) andfor one-third of the active electrodes blurred (5-of-15).

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3.2 Speech-in-noise scores at subject level
The individual SIN scores for the twelve subjects and all MAPs are shown in Fig. 2 for the ALL
condition. A segmented linear regression with two segments was fitted using the “fit” function
provided by MATLAB. The first segment was restricted to very small slope values in the range [-0.1,0.1] and the second segment was restricted to positive slopes in the range [0, 20]. There were nofurther restrictions applied and the same settings were used for all subjects. The knee points of thetwo segments were considered as the threshold at which spectral blurring affected the speech-in-noise
perception at subject level. Knee points varied markedly between subjects and ranged from a spectral
blurring factor of 2 for S11 up to 8 for S2 (the maximum value possible), w ith the other subjects in
between these extremes. Interestingly, subject S2 showed no detrimental effect of spectral blurring
for any MAP, but all other subjects had knee points smaller than 7.
There was a positive relationship between spectral blurring knee points and SIN performance with
M1 across subjects (r = 0.62, df = 10, p = 0.032), meaning that subjects with better SIN performance(lower score) with a MAP that was most similar to their everyday setting were more affected byspectral blurring (lower knee point) than subjects with worse SIN performance. Table 2 shows theperformance scores with M1 for the SIN test and the spectral blurring knee points for all subjects.

Figure 2 – Individual SIN scores for all twelve subjects and all MAPs in the ALL condition. The segmented
linear regression is shown in red and the knee point between the two segments is indicated by a cross.
3.3 Spectro-temporal test scores and relationship with spectral blurring
The results from the spectro-temporal test STRIPES are also shown in Table 2 together with the
SIN M1 scores and the spectral blurring knee points. STRIPES scores varied across subjects over arange of densities from 3.4 up to 7.6 with a mean score of 5. There was a significant negative
relationship between spectral blurring knee points and STRIPES scores across subjects in thepredicted direction (Spearman’s rho = -0.66, df = 10, p = 0.022; STRIPES scores were not normally
distributed according to the Shapiro-Wilk test with W = 0.85, p = 0.036). Figure 3 shows this negative
association and its linear regression.

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Table 2 – Subject-wise spectral blurring knee points and their scores with M1 for SIN and STRIPES.

Subject
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12

Spectralblurring
knee point
5.9
7.0
6.0
3.3
6.0
4.1
5.0
6.0
4.7
6.2
3.0
4.4

SIN
with M1(SRT dB)
17.6
15.4
20.6
12.0
11.8
14.2
11.2
12.2
12.0
16.2
8.6
9.2

STRIPESwith M1(density)
4.3
3.7
3.7
7.1
7.0
7.6
4.1
3.4
3.8
4.6
5.5
4.9

Figure 3 – Association between spectral blurring knee points and STRIPES scores with M1 across subjects.

4. DISCUSSION
We evaluated the effect of spectral blurring on SIN performance in twelve CI users by adjusting
the spectral overlap between electrode channels. In line with our main hypothesis, this led to anincrease in SRTs with spectral blurring for the case when all electrode channels were blurred. Thiseffect was strongest for the most extreme blurring condition M8 and statistical tests confirmed asignificant difference to all other blurring conditions and to the map that was most similar to thesubject’s clinical MAP.
Surprisingly, there was no effect of spectral blurring on SIN performance for the case when one third of the electrodes were blurred, even for the most extreme case with M8b. This was unexpectedin the light of previous research studies on site-specific optimization strategies in CI users, that werebased on the rationale that individual electrode channels can be adjusted to alter SIN performance and,
if done correctly, should lead to improvements. The absence of an effect of spectral blurring on SIN

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performance in the 5-of-15 case may have been due to our choice to distribute the blurred channels
evenly along the electrode array, thereby potentially reducing their impact over a more clusteredselection. The blurred channels may have been compensated by “good” channels that were also evenly
distributed along the array and subjects may have been able to ignore the relatively small number of“bad” channels. However, the choice to evenly distribute the blurred electrodes was based on previous
site-selection studies that also avoided the deactivation of whole segments of the electrode array byusing selection rules that rejected clustering. Also, the proportion of one-third of the electrodes was
similar to previous studies as was the sample size and the evaluation procedure using acute testing.
We note that spectral blurring, as imposed here, simulated only some effects of the channelinteractions that may arise from channels that produce broad current spread. Specifically, in the caseof a subset of electrodes being affected, we simulated to some extent the loss of information conveyed
by those channels, such as might occur due to neural degeneration in the auditory nerve and more
centrally. However, we did not simulate the increased charge interactions that occur between an
electrode that produces a broad current spread and the neighbouring channels. Nevertheless, theresults do show that severely degrading the information conveyed by one third of all available
electrodes has no effect on performance, and this finding should be taken into account when designingsite-selection strategies based on a small subset of distributed electrodes along the array.
Across-subject performance differed markedly in the ALL condition with some subjects being more
affected by spectral blurring than others. The knee points, as a measure of how much blurring was
required to deteriorate SIN performance in a given subject, correlated with the SIN performance acrosssubjects when using the MAP most similar to their clinical MAP. This association may have been due
to an increased interference of the competing talker noise at lower SNRs than at higher SNRs, so that
subjects who tolerated higher levels of noise would have been affected more by an increased overlap
between filter channels than subjects who tolerated lower levels of noise. In general, this association
is in line with the assumption that channel interaction affects SIN performance on an individual basis.
A further advantage of measuring the knee point is that it allows one to distinguish between the effectsof spectral resolution and of more central cognitive factors, both of which can affect speech perce ption
and which are hard to disentangle when measuring performance with a single amount of (or no)
blurring.
The STRIPES test was performed to measure spectro-temporal acuity for the twelve CI subjects to
explore the second hypothesis under test: subjects with high acuity, as indicated by their STRIPES
scores, would be affected more by spectral blurring than subjects with low acuity. A significant
correlation was indeed found. This suggested that the STRIPES test may effectively measure spectral
resolution in a way that is relevant for the perception of speech.

5. CONCLUSIONS
We demonstrated a main effect of spectral blurring on SIN performance in CI subjects for the casewhen all electrodes were blurred but, surprisingly, not for even extreme amounts of blurring when 5
out of 15 electrodes were blurred. This demonstrates a strong resilience of CI users to spectro -temporalsignal distortions and raises the question how much benefit can be obtained by strategies that optimize
only a subset of electrodes that are evenly-spaced along the array. There was a positive relationshipbetween the effect of spectral blurring on SIN performance and the performance with the clinical-like
MAP across subjects, with better-performing subjects being affected by lower amounts of spectral
blurring and vice versa. We also observed the predicted negative relationship between the STRIPES
scores and the effect of spectral blurring on SIN performance across subjects, with more degradation
in SIN performance for subjects with better spectro-temporal acuity. These associations support theassumption that channel interaction in terms of spectral overlap is one of the main factors responsible
for both limited spectro-temporal acuity and SIN performance in CI users. These findings should betaken into account for the design and evaluation site-specific optimization strategies in CIs.

ACKNOWLEDGEMENTS
This work was funded by Action on Hearing Loss (UK, grant number 82). The authors would liketo thank all the subjects for taking part and Dr Alan Archer-Boyd for helpful discussions.

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REFERENCES
1. Friesen LM, Shannon RV, Baskent D, Wang X. Speech recognition in noise as a function of thenumber of spectral channels: Comparison of acoustic hearing and cochlear implants. J Acoust Soc
Am. 2001;110(2)1150-1163.
2. Cullington HE, Zeng FG. Speech recognition with varying numbers and types of competing talkers
by normal-hearing, cochlear-implant, and implant simulation subjects. J Acoust Soc Am.
2008;123(1)450-461.
3. Hu Y, Loizou PC. Environment-specific noise suppression for improved speech intelligibility bycochlear implant users. J Acoust Soc Am. 2010;127(6)3689-3695.
4. Dawson PW, Mauger, SJ, Hersbach AA. Clinical evaluation of signal-to-noise ratio--based noise
reduction in Nucleus. Ear Hear. 2011;32(3)382-390.
5. Goehring T, Bolner F, Monaghan JM, Van Dijk B, Zarowski A, Bleeck S. Speech enhancement basedon neural networks improves speech intelligibility in noise for cochlear implant users. Hear Res.
2017;344;183-194.
6. Henry BA, McKay CM, McDermott HJ, Clark GM. The relationship between speech perception andelectrode discrimination in cochlear implantees. J Acoust Soc Am. 2000;108(3)1269-1280.
7. Srinivasan AG, Padilla M, Shannon RV, Landsberger DM. Improving speech perception in noisewith current focusing in cochlear implant users. Hear Res. 2013;299;29-36.
8. Bierer JA, Litvak L. Reducing channel interaction through cochlear implant programming may
improve speech perception: Current focusing and channel deactivation. Trends in hearing. 2016;20;
2331216516653389.
9. Garadat SN, Zwolan TA, Pfingst BE. Using temporal modulation sensitivity to select stimulationsites for processor MAPs in cochlear implant listeners. Aud. and Neurot. 2013;18(4)247-260.
10. Zhou N. Deactivating stimulation sites based on low-rate thresholds improves spectral ripple andspeech reception thresholds in cochlear implant users. J Acoust Soc Am. 2017;141(3)EL243-EL248.
11. Goehring T, Archer-Boyd A, Deeks JM, Arenberg JG, Carlyon RP. A site-selection strategy based onpolarity sensitivity for cochlear implants: Effects on spectro-temporal resolution and speech
perception. J. Assoc. Res. Otolaryngology. 2019, in press.
12. Archer-Boyd A, Southwell RV, Deeks JM, Turner RE, Carlyon RP. Development and validation ofa spectro-temporal processing test for cochlear-implant listeners. J Acoust Soc Am.
2018;144(5)2983-2997.
13. Bench J, Kowal A, Bamford J. The BKB (Bamford-Kowal-Bench) sentence lists for partiallyhearing children. Brit. J. of Aud. 1979;13(3)108-112.
14. Deeks JM, Carlyon RP. Simulations of cochlear implant hearing using filtered harmonic complexes:
Implications for concurrent sound segregation. J Acoust Soc Am. 2004;115(4)1736-1746.
15. MacLeod A, Summerfield Q. A procedure for measuring auditory and audiovisual speech-reception
thresholds for sentences in noise: Rationale, evaluation, and recommendations for use. Brit. J. of
Aud. 1990;24(1)29-43.

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