Ear Canal Role and Psychoacoustic Parameters: Loudness, Sharpness, Roughness, and Fluctuation Strength
Bibliographic record
Abstract
Abstract Aim: The purpose of this study was to investigate whether psychoacoustic parameters could be significantly influenced by the human ear canal and whether gender played a significant role in these effects. Methods: In this study, sixty participants were enrolled, with an equal distribution of 30 male and 30 female individuals. White and sinusoidal noises at 75, 85, and 95 dB as stimulus sound pressure levels (SSPLs). Psychoacoustic parameters such as loudness, sharpness, roughness, and fluctuation strength were measured both outside (cavum part of the external ear) and inside the right ear of each participant. These measurements were done using a circular-shaped microphone with a 2-mm diameter and Labview software. The duration for each measurement was 10 s. The independent sample t -test and repeated measures ANOVA test were employed for the statistical analysis in this study. The results indicated that the equality of means was rejected at a significance level of P < 0.05. All statistical analyses were conducted using SPSS Version 26. Results: The mean age and standard deviation of all participants were 22.77 ± 2.60 years old. The comparison of psychoacoustic parameters in the exposure to different types of noises at various levels between genders showed no statistically significant differences (all P > 0.05). For both sinusoidal noise and white noise at all three studied SSPLs, the differences in four psychoacoustic parameters between the outer and inner ears of the participants were not statistically significant (all P > 0.05). Conclusion: Based on our findings, it seems that the human ear canal does not have any effect on psychoacoustic parameters, leading us to conclude that the ear canal does not contribute to noise-induced annoyance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".