Sparse regularization for reconstructing transient sources with time domain nearfield acoustical holography
Bibliographic record
Abstract
-norm sparse regularization method is applied to the time domain reconstruction of transient acoustic fields such as impulse noise. This method properly reconstructs the back-propagated sound field where its amplitude should be null: for transient sources, this occurs mostly for positions and times that precede the arrival of the first wave front. Therefore, it significantly reduces causal errors typically found in time domain reconstruction when standard Tikhonov regularizations is applied. The reconstructions obtained from both Tikhonov and sparse regularization methods are compared using a transient baffled piston model, and show that the global root-mean-square (RMS) error is significantly reduced when using sparse regularization. The improvement provided depends on the level of sparsity of the reconstructed signal. For the studied cases, it can represent a reduction of the global RMS error by up to a factor of 3. The performance of Pareto frontier curve for predicting the optimal sparse regularization parameter is examined; it leads to accurate predictions especially for lower noise levels. Finally, sparse regularization is applied to experimental data over time and spatial domains in order to obtain an accurate reconstruction of the transient sound field produced by an impacted plate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".