The association of noise sensitivity with music listening, training, and aptitude
Bibliographic record
Abstract
After intensive, long-term musical training, the auditory system of a musician is specifically tuned to perceive musical sounds. We wished to find out whether a musician's auditory system also develops increased sensitivity to any sound of everyday life, experiencing them as noise. For this purpose, an online survey, including questionnaires on noise sensitivity, musical background, and listening tests for assessing musical aptitude, was administered to 197 participants in Finland and Italy. Subjective noise sensitivity (assessed with the Weinstein's Noise Sensitivity Scale) was analyzed for associations with musicianship, musical aptitude, weekly time spent listening to music, and the importance of music in each person's life (or music importance). Subjects were divided into three groups according to their musical expertise: Nonmusicians (N = 103), amateur musicians (N = 44), and professional musicians (N = 50). The results showed that noise sensitivity did not depend on musical expertise or performance on musicality tests or the amount of active (attentive) listening to music. In contrast, it was associated with daily passive listening to music, so that individuals with higher noise sensitivity spent less time in passive (background) listening to music than those with lower sensitivity to noise. Furthermore, noise-sensitive respondents rated music as less important in their life than did individuals with lower sensitivity to noise. The results demonstrate that the special sensitivity of the auditory system derived from musical training does not lead to increased irritability from unwanted sounds. However, the disposition to tolerate contingent musical backgrounds in everyday life depends on the individual's noise sensitivity.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".