Alternative closeness functions for eye microphone array
Bibliographic record
Abstract
A new signal processing algorithm, accompanied by a novel hemispherical microphone array structure for sound source localization in three-dimensional spaces was presented [H. Alghassi et al., ‘‘Acoustic source localization with eye array,’’ JASA 120(5) (2006)]. This localization methodology, which has some analogy to the eye in localization of light rays, uses concepts of two-microphone (pinhole) or three-microphone (lens) cell structures alongside a special closeness function (CF) to approximate the proximity of the sound source direction to each of the hemisphere microphone directions based on particular similarity measures among signals. The CF plays a major role in the accuracy of the final source direction estimation. The earlier multiplicative CF (MCF) operates based on vector multiplication of spatial derivative and time derivative of microphone signals. This work presents two additional categories of CFs and compares them with MCF. The difference CF (DCF) is based on subtraction of delayed reference signal and shell microphone signals, while the correlative CF (CCF) is based on multiplication of delayed reference signal and shell microphone signals. Similar to MCF, both DCF and CCF perform demonstrated linear output versus deviation angle. Although DCF and MCF have not shown improved experimental accuracy compared to MCF, they attained lower computational complexity.
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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 | 0.002 | 0.010 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".