Effect of Frequency Response Manipulations on Musical Sound Quality for Cochlear Implant Users
Bibliographic record
Abstract
Cochlear implant (CI) users commonly report degraded musical sound quality. To improve CI-mediated music perception and enjoyment, we must understand factors that affect sound quality. In the present study, we utilize frequency response manipulation (FRM), a process that adjusts the energies of frequency bands within an audio signal, to determine its impact on CI-user sound quality assessments of musical stimuli. Thirty-three adult CI users completed an online study and listened to FRM-altered clips derived from the top songs in Billboard magazine. Participants assessed sound quality using the MUltiple Stimulus with Hidden Reference and Anchor for CI users (CI-MUSHRA) rating scale. FRM affected sound quality ratings (SQR). Specifically, increasing the gain for low and mid-range frequencies led to higher quality ratings than reducing them. In contrast, manipulating the gain for high frequencies (those above 2 kHz) had no impact. Participants with musical training were more sensitive to FRM than non-musically trained participants and demonstrated preference for gain increases over reductions. These findings suggest that, even among CI users, past musical training provides listeners with subtleties in musical appraisal, even though their hearing is now mediated electrically and bears little resemblance to their musical experience prior to implantation. Increased gain below 2 kHz may lead to higher sound quality than for equivalent reductions, perhaps because it offers greater access to lyrics in songs or because it provides more salient beat sensations.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".