Direction‐of‐arrival estimation for far‐field acoustic signal in presence of near‐field interferences
Bibliographic record
Abstract
The far‐field acoustic signal received by an acoustic array is frequently affected by near‐field interferences. This causes deterioration of the direction‐of‐arrival (DOA) estimate for the far‐field signal. To enhance the DOA estimate, the novel near‐field/far‐field (NFFF) beamformer is proposed. Such a beamformer optimises the beam pattern for far‐field detection by maximising the beamformer output in the direction of the far‐field target signal with the imposed condition to eliminate interfering signals from near‐field locations. As the interference suppression only occurs at specific positions of near‐field interferences, a blind zone in the far‐field direction present in conventional methods will not be introduced. The NFFF beamformer is also applicable for coherent signals and for multi‐interferers. For a stationary situation where interferers’ locations are fixed, the NFFF beamformer computations do not require time updates and the computational load is similar to that of the conventional beamformer. The method can be extended to several situations such as acoustic monitoring performed from a stationary platform subjected to water currents, waves, winds and other variables, all of them generating nearby interferences, and also to different array configurations including two‐dimensional (2D) and 3D 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".