Beamforming beyond the λ/2 limit with microphone arrays
Bibliographic record
Abstract
One limitation of microphone arrays is that the inter-microphone spacing is restricted to λ/2 of the shortest wavelength (highest frequency) of interest. For an increase in frequency range, the array must either be made smaller (thereby losing low-frequency directivity) or the number of microphones must be increased (thereby increasing cost). The other problem is that the beamwidth decreases with increasing frequency and sidelobes become more problematic. This results in significant off-axis ‘‘coloration’’ of the signals. The extension of the working frequency range for an existing narrow-band (300–3 kHz) telephony microphone array to wide-band telephony (up to 7 kHz), without modifying its geometry and the number of microphones will be shown. Microphones are embedded in a diffraction structure that provides the desired directivity at high frequencies. To provide the desired directionality at lower frequencies, beamforming of the microphones is performed using digital signal processing techniques. The combination of beamforming and embedding the microphones in a diffraction structure that provides the desired directivity at high frequencies addresses the two weaknesses that arise in previous approaches: low-frequency directivity with small arrays and high-frequency difficulties that arise in conventional sensor arrays. [Work supported in part by Carleton University.]
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".