Sound quality of side-by-side vehicles: Investigation of multidimensional sensory profiles and loudness equalization in an industrial context
Bibliographic record
Abstract
The sensory perception of products influences the relationship of potential users or buyers with these products. Sound quality is part of this sensory experience and is critical for products such as sports or utility vehicles as the sound conveys the impression of power or efficiency, among others. Therefore, there is a need to provide tools based on scientific methodology to acoustical engineers designing such vehicles. The motivation of this work was the need to explore new and faster methods for quicker and simpler sound quality evaluation. In this paper, the sound quality of side-by-side utility vehicles is investigated using the rapid sensory profile measurement method, and then by creating virtual participants using bootstrapping methods. Additionally, this study also investigates the effect of loudness equalization of the sound samples used during the listening tests. Results from these studies were used to establish the sensory profiles, desire-to-buy values and desirable sound profiles regarding the tested vehicles. Equalized loudness tests provide a finer sensory profile than those obtained using non-equalized sound samples. Furthermore, statistical analysis results confirm that adding virtual participants to the original data using a bootstrapping approach helps highlighting key information without altering the validity of the results.
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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 | 0.000 | 0.000 |
| 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".