Bibliographic record
Abstract
Wave field synthesis (WFS) is a sound field reproduction technology which assumes that the reproduction environment is anechoic. A real reproduction space reduces the objective accuracy of WFS. This research involves the improvement of WFS performance by active noise control and adaptive filters. Simulations of sound field reproduction in a three-dimensional space show the physical possibility of progressive sound field reproduction in a closed space on the basis of optimal control of the reproduction errors at a sensor array. The proposed solution is adaptive wave field synthesis (AWFS). The originality of AWFS is the combination of the minimization of the reproduction error along with a penalty for any departure from the WFS solution. AWFS is theoretically analyzed on the basis of singular value decomposition. This suggests that the AWFS underlying mechanism is independent radiation mode control. Two adaptive algorithms for AWFS are developed. An experimental AWFS system is tested in different rooms (hemi-anechoic, laboratory, reverberant). It shows that AWFS performs better than WFS: AWFS significantly reduces the room effects on sound field reproduction. The algorithm based on independent radiation mode control contributes to the enlargement of the effective reproduction region. The thesis is written in French and in English.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".