Adaptive wave field synthesis for active sound field reproduction: Experimental results
Bibliographic record
Abstract
Sound field reproduction has applications in music reproduction, spatial audio, sound environment reproduction, and experimental acoustics. Sound field reproduction can be used to artificially reproduce the spatial character of natural hearing. The objective is then to reproduce a sound field in a real reproduction environment. Wave field synthesis (WFS) is a known open-loop technology which assumes that the reproduction environment is anechoic. The room response thus reduces the quality of the physical sound field reproduction by WFS. In recent research papers, adaptive wave field synthesis (AWFS) was defined as a potential solution to compensate for these quality reductions from which WFS objective performance suffers. In this paper, AWFS is experimentally investigated as an active sound field reproduction system with a limited number of reproduction error sensors to compensate for the response of the listening environment. Two digital signal processing algorithms for AWFS are used for comparison purposes, one of which is based on independent radiation mode control. AWFS performed propagating sound field reproduction better than WFS in three tested reproduction spaces (hemianechoic chamber, standard laboratory space, and reverberation chamber).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".