Time domain nearfield acoustical holography with three-dimensional linear deconvolution
Bibliographic record
Abstract
In this paper, a formulation is proposed to improve the time domain reconstruction of non-stationary acoustic fields with nearfield acoustical holography. The formulation involves applying three-dimensional (3D) linear deconvolution using a Green's function sampled in the time and spatial domains. Because linear deconvolution does not assume periodic signals, it more appropriately describes the decaying behavior of sources that begin and end at null amplitude and that radiate over a finite amount of time. The proposed method outperforms standard circular convolution-based nearfield acoustical holography by up to a factor three in relative root-mean-square error, when compared using a transient baffled piston model, and its reconstructions remain accurate over large back-propagation distances. Furthermore, it is shown that truncation errors in linear deconvolution can be reduced by applying a 3D patch extrapolation algorithm; however, convergence depends on the choice of an adequate Tikhonov's regularization parameter. Three methods for predicting the optimal parameter are compared: the L-curve, the generalized cross-validation, and the empirical Bayesian method. It is shown that with the proposed formulation applied to reconstructing the field radiated by a transient baffled piston, the generalized cross-validation gives the overall best prediction for the noise levels and back-propagation distances studied.
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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 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".