Effect of Musical Training on Musical Perception and Hearing Sensitivity: Conventional and High-Frequency Audiometric Comparison
Bibliographic record
Abstract
This prospective study was designed to investigate the role of musical training on musical perception and hearing acuity and to determine probable hearing loss. Thirty students, aged 17 to 23 years, were evaluated for hearing sensitivity within conventional and high-frequency audiometric ranges. The hearing thresholds of the controls were compared with those of the students. To evaluate the effect of musical training on musical perception, students were given an examination consisting of single-note, harmonic hearing; multiple sounds (chords with two, three, and four sounds), horizontal hearing; melody, and rhythm. Musical perception and the average hearing level of the students on admission to the faculty were compared with the data from students after a 2-year musical education program. The hearing sensitivity of the students at the initial and final evaluations was not similar. The average hearing acuity increased for the whole conventional audiometric range (p < .05). There was worsening for 12, 14, and 16 kHz for the high-frequency audiometric range (p < .05). The decrease in average hearing acuity at these frequencies was statistically significant, as indicated by Student's t-test (p < .05). Although the average musical hearing sensitivity increased for horizontal hearing (p < .05), it did not change for harmonic hearing (p > .05). Musical training might increase the spontaneous attention to the sound heard and the ability to discriminate. Hearing reduction at the high frequencies might be attributed to continuous noise exposure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".