^{1,a)}, David A. Nelson

^{1}, Heather Kreft

^{1}, Peggy B. Nelson

^{2}and Andrew J. Oxenham

^{3}

### Abstract

Spectral ripple discrimination thresholds were measured in 15 cochlear-implant users with broadband (350–5600 Hz) and octave-band noise stimuli. The results were compared with spatial tuning curve (STC) bandwidths previously obtained from the same subjects. Spatial tuning curve bandwidths did not correlate significantly with broadband spectral ripple discrimination thresholds but did correlate significantly with ripple discrimination thresholds when the rippled noise was confined to an octave-wide passband, centered on the STC’s probe electrode frequency allocation. Ripple discrimination thresholds were also measured for octave-band stimuli in four contiguous octaves, with center frequencies from 500 Hz to 4000 Hz. Substantial variations in thresholds with center frequency were found in individuals, but no general trends of increasing or decreasing resolution from apex to base were observed in the pooled data. Neither ripple nor STC measures correlated consistently with speech measures in noise and quiet in the sample of subjects in this study. Overall, the results suggest that spectral ripple discrimination measures provide a reasonable measure of spectral resolution that correlates well with more direct, but more time-consuming, measures of spectral resolution, but that such measures do not always provide a clear and robust predictor of performance in speech perception tasks.

This work was supported by a grant from the National Institutes of Health (R01 DC 006699) and by the Lions International Hearing Foundation. We thank Christophe Micheyl for help with the statistical analyses, Leo Litvak of Advanced Bionics for providing the matlab code for the spectral ripple computer simulations, and our research subjects for their participation.

I. INTRODUCTION

II. EXPERIMENT 1: BROADBAND RIPPLE DISCRIMINATION

A. Subjects

B. Stimuli

C. Procedure

D. Comparison with spatial tuning curve bandwidths

E. Results and discussion

F. Experiment 1b: Effects of spectral edges on ripple discrimination

III. EXPERIMENT 2: COMPARISON OF OCTAVE-BAND SPECTRAL RIPPLE DISCRIMINATION AND SPATIAL TUNING CURVES OBTAINED FROM THE SAME REGION OF THE COCHLEA

A. Rationale

B. Methods

C. Results and discussion

IV. EXPERIMENT 3: FIXED OCTAVE-BAND RIPPLE DISCRIMINATION

A. Rationale

B. Methods

C. Results

D. Discussion

V. COMPARISONS WITH MEASURES OF SPEECH PERCEPTION

A. Rationale

B. Methods

C. Results

1. Broadband ripple discrimination and speech recognition

2. STC bandwidth and speech recognition

D. Discussion

VI. GENERAL DISCUSSION

A. Spectral ripple discrimination as a measure of spectral resolution

B. Relationships between spatial tuning curves, spectral ripple discrimination, and speech perception

VII. CONCLUSIONS

### Key Topics

- Electrodes
- 41.0
- Speech
- 34.0
- Speech recognition
- 28.0
- Cochlear implants
- 25.0
- Sound discrimination
- 17.0

## Figures

Spatial tuning curve for subject D08, measured in a previous study (Nelson *et al.*, 2008). Depicted by the black squares in this figure is the current level needed to just mask a low-level probe presented to electrode 8, as a function of masker electrode number. Stimulus amplitude (*µ*A) is shown on the ordinate, with research electrode number (rEL) displayed on the abscissa. (The rEL numbering system normalizes the different numbering systems used by different implant devices; number 1 is assigned to the most apical electrode, with consecutive numbering proceeding to the basal end of the array.) Error bars indicate standard deviations. Gray squares represent current levels at maximum acceptable loudness (MAL) for each electrode; gray diamonds indicate current levels at threshold (THS) for each electrode. The open circle at the tip of the curve indicates the probe electrode, with probe level shown by the symbol’s vertical position and sensation level indicated by the height of the vertical line beneath it. Tuning curve bandwidth (BW) is defined as the width of the STC at 1 dB above the tip of the STC (Q1 dB Level).

Spatial tuning curve for subject D08, measured in a previous study (Nelson *et al.*, 2008). Depicted by the black squares in this figure is the current level needed to just mask a low-level probe presented to electrode 8, as a function of masker electrode number. Stimulus amplitude (*µ*A) is shown on the ordinate, with research electrode number (rEL) displayed on the abscissa. (The rEL numbering system normalizes the different numbering systems used by different implant devices; number 1 is assigned to the most apical electrode, with consecutive numbering proceeding to the basal end of the array.) Error bars indicate standard deviations. Gray squares represent current levels at maximum acceptable loudness (MAL) for each electrode; gray diamonds indicate current levels at threshold (THS) for each electrode. The open circle at the tip of the curve indicates the probe electrode, with probe level shown by the symbol’s vertical position and sensation level indicated by the height of the vertical line beneath it. Tuning curve bandwidth (BW) is defined as the width of the STC at 1 dB above the tip of the STC (Q1 dB Level).

Ripple discrimination thresholds as a function of transformed STC bandwidth (from Nelson *et al.*, 2008). (a) Broadband ripple discrimination thresholds, in ripples per octave (rpo) with a linear regression fit to all 15 data points; (b) the regression line for the least squares fit to data from 13 subjects, excluding subjects C05 and D10.

Ripple discrimination thresholds as a function of transformed STC bandwidth (from Nelson *et al.*, 2008). (a) Broadband ripple discrimination thresholds, in ripples per octave (rpo) with a linear regression fit to all 15 data points; (b) the regression line for the least squares fit to data from 13 subjects, excluding subjects C05 and D10.

Plots of windowed (left) and non-windowed (right) stimulus spectra. Stimuli are broadband (350–5600 Hz) noise with sinusoidal spectral ripples; spectral modulation frequency is 1 ripple per octave. The windowed stimulus includes Hanning (raised-cosine) ramps applied to the spectral edges. The non-windowed stimulus has steep spectral edges.

Plots of windowed (left) and non-windowed (right) stimulus spectra. Stimuli are broadband (350–5600 Hz) noise with sinusoidal spectral ripples; spectral modulation frequency is 1 ripple per octave. The windowed stimulus includes Hanning (raised-cosine) ramps applied to the spectral edges. The non-windowed stimulus has steep spectral edges.

Ripple discrimination thresholds for shallow-sloped (windowed) stimuli, with half-octave Hanning ramps on each side, as a function of thresholds for steep-sloped (non-windowed) stimuli. The diagonal line represents perfect correspondence between the two types of stimuli (slope = 1).

Ripple discrimination thresholds for shallow-sloped (windowed) stimuli, with half-octave Hanning ramps on each side, as a function of thresholds for steep-sloped (non-windowed) stimuli. The diagonal line represents perfect correspondence between the two types of stimuli (slope = 1).

Octave-band ripple discrimination (from Experiment 2) as a function of transformed STC bandwidth. Data from all subjects are included in the solid regression line; subjects C05 and D10 are identified with different symbol shapes. Data from 13 subjects, excluding C05 and D10, are included in the dotted regression line.

Octave-band ripple discrimination (from Experiment 2) as a function of transformed STC bandwidth. Data from all subjects are included in the solid regression line; subjects C05 and D10 are identified with different symbol shapes. Data from 13 subjects, excluding C05 and D10, are included in the dotted regression line.

Ripple discrimination thresholds as a function of low-frequency cutoff of octave-band rippled noise stimuli, for 15 individual subjects, separated by device type. (a) Performance of Clarion I subjects; (b) Clarion II subjects; and (c) Nucleus 22 subjects.

Ripple discrimination thresholds as a function of low-frequency cutoff of octave-band rippled noise stimuli, for 15 individual subjects, separated by device type. (a) Performance of Clarion I subjects; (b) Clarion II subjects; and (c) Nucleus 22 subjects.

(a) Sentence recognition in quiet (rau scores) as a function of broadband ripple discrimination threshold. (b) Vowel recognition in quiet (rau scores) as a function of broadband ripple discrimination threshold. (c) SNR for 50% correct sentence recognition, interpolated/extrapolated from performance-intensity (P-I) functions, as a function of broadband ripple discrimination threshold. Data from 12 subjects are included. (d) SNR for 50% correct vowel recognition, which was interpolated/extrapolated from P-I functions for 14 subjects, as a function of broadband ripple discrimination threshold. Regression lines are all logarithmic fits, with the exception of (c), which shows a linear fit.

(a) Sentence recognition in quiet (rau scores) as a function of broadband ripple discrimination threshold. (b) Vowel recognition in quiet (rau scores) as a function of broadband ripple discrimination threshold. (c) SNR for 50% correct sentence recognition, interpolated/extrapolated from performance-intensity (P-I) functions, as a function of broadband ripple discrimination threshold. Data from 12 subjects are included. (d) SNR for 50% correct vowel recognition, which was interpolated/extrapolated from P-I functions for 14 subjects, as a function of broadband ripple discrimination threshold. Regression lines are all logarithmic fits, with the exception of (c), which shows a linear fit.

(a) Sentence recognition in quiet (rau scores) as a function of 1/BW. (b) Vowel recognition in quiet (rau scores) as a function of 1/BW. Regression lines are log fits. (c) SNR for 50% correct sentence recognition, interpolated/extrapolated from P-I functions, as a function of 1/BW. The regression line is a linear fit. Data from 12 subjects are included. (d) SNR for 50% correct vowel recognition, which was interpolated/extrapolated from P-I functions for 14 subjects, as a function of 1/BW. The regression line is a linear fit.

(a) Sentence recognition in quiet (rau scores) as a function of 1/BW. (b) Vowel recognition in quiet (rau scores) as a function of 1/BW. Regression lines are log fits. (c) SNR for 50% correct sentence recognition, interpolated/extrapolated from P-I functions, as a function of 1/BW. The regression line is a linear fit. Data from 12 subjects are included. (d) SNR for 50% correct vowel recognition, which was interpolated/extrapolated from P-I functions for 14 subjects, as a function of 1/BW. The regression line is a linear fit.

Output patterns based on characteristics of one C-I subject and one C-II subject. Electrode number is shown on the abscissa, with current output (in arbitrary units) displayed on the ordinate. The top row of panels shows four stimulation patterns for an 8-channel CIS processor, and the bottom row simulates a 16-channel processor. From left to right, the plots illustrate the output for rippled noise stimuli of 0.5, 1, 2, and 4 rpo.

Output patterns based on characteristics of one C-I subject and one C-II subject. Electrode number is shown on the abscissa, with current output (in arbitrary units) displayed on the ordinate. The top row of panels shows four stimulation patterns for an 8-channel CIS processor, and the bottom row simulates a 16-channel processor. From left to right, the plots illustrate the output for rippled noise stimuli of 0.5, 1, 2, and 4 rpo.

## Tables

Summary of CI subject characteristics. The subject identifiers C, D, and N denote Clarion I, Clarion II, and Nucleus users, respectively.

Summary of CI subject characteristics. The subject identifiers C, D, and N denote Clarion I, Clarion II, and Nucleus users, respectively.

Individual STC data (from Nelson *et al.*, 2008).

Individual STC data (from Nelson *et al.*, 2008).

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