Multi-Level Method for Sound Source Location Measurement
Bibliographic record
Abstract
This paper presents a novel multi-level search method that enhances the Delay-and-Sum beamformer with an interactive search to identify the location of a sound source in a 3-D space accurately and in a computationally efficient manner. In this method, the delay-and-sum beamformer estimates the sound source location within a cubical search space, with the side length of this cube decreasing in each iteration by a specified reduction factor. In each iteration, a search is conducted within a new cubical space centered on the previously estimated source location. The search process is iteratively refined until estimations reach a state of convergence. Furthermore, our findings across 5 different reduction factors ranging from 0.55 to 0.95 reveal that the choice of reduction factor affects both convergence rate and accuracy; reduction factors closer to 1 provide slower convergence and lower distance error to the actual source, whereas smaller reduction factors provide faster convergence and higher distance error.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".