Extending interior near-field acoustic holography to visualize three-dimensional objective parameters of sound quality
Bibliographic record
Abstract
It is essential to understand that the ultimate goal of interior noise control is to improve the sound quality inside the vehicle, rather than to suppress the sound pressure level. Therefore, the vehicle interior sound source localization and identification should be based on the contributions of sound sources to the subjective and/or objective parameters of sound quality at targeted points, such as driver’s ear positions. This talk introduces the visualization of three-dimensional objective parameters of sound quality based on interior near-field acoustic holography (NAH). The methodology of mapping three-dimensional sound pressure distribution, which is reconstructed based on interior NAH, to three-dimensional loudness is presented. The mathematical model of loudness developed by ANSI standard is discussed. The numerical interior sound field, which is generated by vibrating enclosure with known boundary conditions, is employed to validate the methodology. In addition, the accuracy of reconstruction of loudness distribution is examined with ANSI standard and digital head. It is shown that the results of sound source localization based on three-dimensional loudness distribution are different from the ones based on interior NAH.
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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 | 0.000 |
| 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) | 0.000 | 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".