Experimental assessment of the performance of a “smart foam” absorber
Bibliographic record
Abstract
The signal processing implementation of a hybrid passive/active absorber (smart foam) made up from the combination of a passive absorbent (poroelastic melamine foam) typically efficient at the high frequencies, and a bonded, curved PVDF film, to enhance low frequency performance is considered. Three methods for obtaining the control signal are experimentally compared in the case of a plane wave excitation. A prototype of such smart foam has been built and tested in a waveguide (rectangular impedance tube) at frequencies between 100 Hz and 1000 Hz. The performances in term of the ratio of reflected to incident sound pressure are presented and discussed, comparing the three methods. The first method uses estimations of the transfer functions between the sources and two microphones in the tube to calculate off-line an optimal filter in the frequency domain. It is then compared with numerical simulations, based on a 3D FE model of the smart foam. The two other methods are based on a real-time adaptive control using a FXLMS algorithm, a unidirectional microphone as error sensor, and the primary source signal as reference signal. The control filter is obtained after the initial convergence, using either pure tone disturbance (secnd method) or broad band disturbance (third method).
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".