Comparison of time domain noise source localization techniques: Application to impulsive noise of nail guns
Bibliographic record
Abstract
Microphone array techniques are an efficient tool to detect acoustic source positions. The standard technique is the delay and sum beamforming. In the time domain, the generalized cross correlation of the microphone signals is used to compute the Spatial Likelihood Functions (SLF) of all microphone pairs and the noise source map is provided by the arithmetic mean of these functions. To improve the former noise source map, which means narrowing the main lobe and removing side and spurious lobes, several techniques have been developed in the past. In this work, the performances of three of these techniques (in terms of source position detection, amplitude estimation and computation time) are compared in the case of both synthetic and real data: (1) energetic and geometric criteria are applied in order to remove the SLF with useless information, (2) the arithmetic mean is replaced by the generalized mean and (3) linear inverse problem is solved with sparsity constraint. In the case of real data, the source to be located and quantified is an impulsive noise radiated by nail guns which is recorded by a spiral arm microphone array.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".