Anechoic measurements of particle-velocity probes compared to pressure gradient and pressure microphones
Bibliographic record
Abstract
Microflown probes are true figure-of-eight-pattern velocity microphones having extended response down to below the lowest audible frequencies, low noise, and high output. Unlike pressure-gradient microphones, velocity probes do not measure acoustic pressure at two points to derive a pressure gradient. When particle velocity is present, acoustical particle velocity sensors measure the temperature difference of the two closely spaced and heated platinum wire resistors, and quantify particle velocity from the temperature measurement. Microflown probes do not require a membrane and the associated mechanical vibration system. A number of anechoic measurements of velocity probes are compared to measurements of pressure-gradient and pressure microphones made under identical acoustical conditions at varying distances from a point source having a wide frequency response. Detailed measurements show specific response changes affected by the distance to the source, and focus on the importance of transducer calibration with respect to distance. Examples are given from field work using microflown probes to record acoustic response of rooms to test signals. The probe’s cosine directional selectivity can be used to change the ratio between early reflections and the diffuse sound since only the 1/3 of the power in the diffuse sound field is measured with the particle velocity probe.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".