A Criterion Based on the Calculation of a Solid Angle to Assess the Quality of Acoustic Images Obtained With a SMA
Bibliographic record
Abstract
The quality of an acoustic image is evaluated based on criteria such as the source level, the mainlobe-to-sidelobes ratio and the spatial resolution. Spatial resolution can be evaluated by the mainlobe area. For spherical microphone arrays, polar projection and ellipse area can be used. These methods are however applied in a plane and not in a spherical plane. In particular, the covariance ellipsemethod may exceed the spherical coordinate frame of the acoustic image when the mainlobe is large, typically at low frequencies, or when there are numerous sidelobes at high frequencies. In addition, the mainlobe may not be centered in the acoustic image and may be distorted around the poles. In order to overcome these limitations, a criterion based on the solid angle is introduced in this paper.The performance of the method based on the solid angle computation is compared with that based on the ellipse. Numerical simulations are performed using a spherical microphone array with a rigid t-design geometry, and spherical harmonics are used to generate the acoustic images. The results show that the solid angle method provides a more stable value compared to the covariance ellipse method when the source is not centered. When the source is centered, the results are similar except when sidelobes occur. In conclusion, the solid angle method provides a robust approach for evaluating the spatial resolution of acoustic images obtained with spherical microphone arrays.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".