Tonal Metrics in the Presence of Masking Noise and Correlation to Subjective Assessment
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">As the demand for Sound Quality improvements in vehicles continues to grow, robust analysis methods must be established to clearly represent end-user perception. For vehicle sounds which are tonal by nature, such as transmission or axle whine, the common practice of many vehicle manufacturers and suppliers is to subjectively rate the performance of a given part for acceptance on a scale of one to ten. The polar opposite of this is to measure data and use the peak of the fundamental or harmonic orders as an objective assessment. Both of these quantifications are problematic in that the former is purely subjective and the latter does not account for the presence of masking noise which has a profound impact on a driver's assessment of such noises.</div><div class="htmlview paragraph">This paper presents the methodology and results of a study in which tonal noises in the presence of various level of masking noise were presented to a group of jurors in a controlled environment. Their subjective ratings were collected and correlated to noise and vibration metrics. These findings were incorporated within a custom software application which imports a test data file, determines the present masking level, and assigns a rating for the tonal noises above masking noise which is robustly correlated to the subjective impressions of a large test sample of end users. This data collection and analysis procedure has been adopted as a standard procedure within a major American vehicle manufacturer.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".