Influence of the Scattering Effect on Acoustic Image Obtained with a Spherical Microphone Array
Bibliographic record
Abstract
Longterm exposure to noise in the workplace can lead hearing loss and other psychosocial effects. Among the most effective noise reduction methods is to apply efforts directly at the source. To do so, each source must be characterized by their spatial location and contribution. In the workplace, a Spherical Microphone Array (SMA) can be used. When the microphones are held on a wireframe structure or on thin rods, the SMA is considered acoustically transparent, and the Conventional Beamforming in the Frequency domain (CBF) algorithm can be used. The CBF, however, does not compensate for the scattering effect of a diffracting object. On the other hand, rigid SMAs are usually made of a solid sphere with flush-mounted microphones and may scatter the acoustic waves. Although the literature has shown the advantages of using a rigid SMA with the spherical harmonics decomposition, the influence of the scattering effect on the acoustic image when not accounted for remains under-examined. This study aims to assess the influence of the uncompensated scattering effects on acoustic images obtained with a SMA. Images obtained with a rigid SMA are compared to those obtained from a theoretical perfectly transparent SMA using the CBF. First, a spherical wave field is simulated using a finite element analysis model to generate the microphone signals for both SMAs. Then, the images are generated using the CBF and three image quality criteria are used to assess the scattering effects, i.e., the ellipse area ratio, the mainlobe-to-sidelobe ratio and the mainlobe level. Results show that the influence of the scattering effect, if not corrected, will reduce the width of the mainlobe and amplify the level of the sidelobes. The effect on the estimated source level is not significant in this case. © 2023, The authors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".