Structure Borne Insertion Loss of Sound Package Components
Bibliographic record
Abstract
Typical automotive sound package components are usually characterized by their absorption coefficients and their acoustic power-based insertion loss. This insertion loss (IL) is usually obtained by subtracting the transmission loss (TL) of a bare flat steel plate from the TL of the same plate covered with the trim material. While providing useful information regarding the performance of the component, air-borne insertion loss is based solely on acoustic excitations and thus provides very little information about the structure-borne performance of the component. This paper presents an attempt to introduce a standard procedure to define the power-based structure-borne insertion loss of sound package components. A flat steel plate is excited mechanically using a shaker. Different carpet constructions are applied on the plate and tested. Based on velocity measurements, a force transducer and intensity probe, the mechanical input and the acoustic radiated power are obtained. Structure-borne insertion loss is then calculated from the input and radiated power measurements. Details of the technique are described and results for different configurations are presented and discussed. In order to analyze the governing physical mechanisms, low and high frequency numerical simulations are studied. The numerical approach is based on two methods;1) the low frequency spectrum is covered using a finite element formulation,2) a transfer matrix approach, modified to incorporate mechanical excitations, is used for the high frequency portion of the spectrum.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".