Sound Environment Reproduction for Health and Safety Studies Using Microphone Arrays, Wave Field Synthesis and the Lasso Minimizer
Bibliographic record
Abstract
Industrial workplaces often offer poor intelligibility and audibility of alarms. One of the most important sound signals on the workplace is the reversing alarm. Incidents related to the audibility of these alarms range from minor injuries to death. Perceptual studies are required to mitigate that risk with appropriate solutions. Since it is difficult to conduct large perceptual studies on site, sound field reproduction of workers' sound environments is an interesting avenue. This paper presents the sound field reproduction of working environments using microphone arrays, wave field synthesis, and the lasso minimizer (1-norm regularization). The aim is to experimentally verify the potential of the lasso to increase the spatial contrast, or precision, since it favors sparsity in the driving signals. Indeed, there is a spatial blur often obtained using inverse problem with 2-norm regularization which leads to non-sparse solution. At the light of the experimental results, accurate in-laboratory sound field reproduction of working environments is possible. The lasso minimizer can enhance the spatial contrast of the loudspeaker driving signal.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".