Eye array dereverberation by corner placement
Bibliographic record
Abstract
A new signal processing algorithm accompanied with a novel array structure for sound source localization (SSL) in three-dimensional spaces was presented [Hedayat Alghassi et al., J. Acoust. Soc. Am. 120(5) (2006)]. This methodology has some analogy to the eye. The microphone array’s proper placement is particularly important in enclosed areas, where reverberation is the dominant problem. It will be demonstrated that by placing the eye array’s symmetry axis along the boresight axis of an upper orthogonal trihedral corner in an enclosed area, the adverse effect of early reverberation can be substantially reduced. This enhancement is the consequence of both the retroreflection property of the orthogonal trihedral corners plus the eye array’s insensitivity to back reflections. There is a pseudo-coincidence between the estimation cells and the source direction in the eye array SSL method. Therefore, the majority of the early reflections from the trihedral corner strike the estimation cells in the direction opposite to the sound source, where the sensitivity is lowest. This approach not only reduces the adverse effect of reverberation on the SSL accuracy, but also creates an approximately flat error for all source directions. Experiments confirmed that this method achieves a reasonable accuracy improvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".