Cochlear Implant Compression Optimization for Musical Sound Quality in MED-EL Users
Bibliographic record
Abstract
OBJECTIVES: Variations in loudness are a fundamental component of the music listening experience. Cochlear implant (CI) processing, including amplitude compression, and a degraded auditory system may further degrade these loudness cues and decrease the enjoyment of music listening. This study aimed to identify optimal CI sound processor compression settings to improve music sound quality for CI users. DESIGN: Fourteen adult MED-EL CI recipients participated (Experiment No. 1: n = 17 ears; Experiment No. 2: n = 11 ears) in the study. A software application using a modified comparison category rating (CCR) test method allowed participants to compare and rate the sound quality of various CI compression settings while listening to 25 real-world music clips. The two compression settings studied were (1) Maplaw, which informs audibility and compression of soft level sounds, and (2) automatic gain control (AGC), which applies compression to loud sounds. For each experiment, one compression setting (Maplaw or AGC) was held at the default, while the other was varied according to the values available in the clinical CI programming software. Experiment No. 1 compared Maplaw settings of 500, 1000 (default), and 2000. Experiment No. 2 compared AGC settings of 2.5:1, 3:1 (default), and 3.5:1. RESULTS: In Experiment No. 1, the group preferred a higher Maplaw setting of 2000 over the default Maplaw setting of 1000 (p = 0.003) for music listening. There was no significant difference in music sound quality between the Maplaw setting of 500 and the default setting (p = 0.278). In Experiment No. 2, a main effect of AGC setting was found; however, no significant difference in sound quality ratings for pairwise comparisons were found between the experimental settings and the default setting (2.5:1 versus 3:1 at p = 0.546; 3.5:1 versus 3:1 at p = 0.059). CONCLUSIONS: CI users reported improvements in music sound quality with higher than default Maplaw or AGC settings. Thus, participants preferred slightly higher compression for music listening, with results having clinical implications for improving music perception in CI users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".