Extraction of infrasonic waveforms from conventional condenser microphones
Bibliographic record
Abstract
There are several commercial measurement-grade condenser microphones that are suitable for measurements down to about 0.1 Hz; however, these one-half-inch microphones reach those low frequencies with an in-line attenuator that compromises the overall dynamic range. In a previous paper [T. M. Marston and T. B. Gabrielson, J. Acoust. Soc. Am. 119, 3378 (2006)], a process was described for digital reconstruction of the lowest frequencies that avoids the attenuator and the sacrifice in dynamic range. This work has been extended to evaluate a reconstruction process for a one-quarter-inch condenser microphone, which has significantly higher low-frequency roll-off than the one-half-inch infrasonic microphones. One reason for using the one-quarter-inch microphone is its higher peak pressure limit. By modeling the preamplifier input-impedance bootstrapping and the pressure-equalization leak, extension to a few tenths of a hertz can be performed. However, the pressure-equalization leak characteristic frequency varies from microphone to microphone and with temperature. Consequently, there is a single free parameter that requires a measurement of the actual very-low-frequency response or measurement of a physically constrained reference waveform (like the N-wave from a clean sonic boom). [Work supported by the FAA/NASA/Transport-Canada Center of Excellence for Aircraft Noise and Aviation Emissions Mitigation.]
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".