Reverberation Time Influences Musical Enjoyment With Cochlear Implants
Bibliographic record
Abstract
OBJECTIVE: To identify factors that enhance the enjoyment of music in cochlear implant (CI) recipients. Specifically, we assessed the hypothesis that variations in reverberation time (RT60) may be linked to variations in the level of musical enjoyment in CI users. STUDY DESIGN: Prospective analysis of music enjoyment in normal-hearing individuals. SETTING: Single tertiary academic medical center. PATIENTS: Normal-hearing adults (N = 20) were asked to rate a novel 20-second melody on three enjoyment modalities: musicality, pleasantness, and naturalness. INTERVENTION: Subjective rating of music excerpts. MAIN OUTCOME MEASURES: Participants listened to seven different instruments play the melody, each with five levels (0.2, 1.6, 3.0, 5.0, 10.0 s) of RT60, both with and without CI simulation processing. Linear regression analysis with analysis of variance was used to assess the impact of RT60 on music enjoyment. RESULTS: Without CI simulation, music samples with RT60 = 3.0 seconds were ranked most pleasant and most musical, whereas those with RT60 = 1.6 seconds and RT60 = 3.0 seconds were ranked equally most natural (all p < 0.05). With CI simulation, music samples with RT60 = 0.2 seconds were ranked most pleasant, most musical, and most natural (all p < 0.05). Samples without CI simulation show a preference for middle-range RT60, whereas samples with CI simulation show a negative linear relationship between RT60 and musical enjoyment, with preference for minimal reverberation. CONCLUSION: Minimization of RT60 may be a useful strategy for increasing musical enjoyment under CI conditions, both in altering existing music as well as in composition of new music.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".