Low frequency acoustic test cell for the evaluation of circumaural headsets and hearing protection
Bibliographic record
Abstract
Active noise reduction (ANR) technology, based on feedback signal processing, is being applied in commercial communication headsets and provides noise reductions up to 10-20 dB between 50 Hz and 400 Hz. There are, however, many acoustical designs and computational difficulties associated with feedback designs which limit their performance. Current research in feedforward design offers the opportunity for significant improvement in ANR performance. To support this current research in ANR feedforward algorithm development and evaluation, a low frequency acoustic test cell (LFATC) has been designed to provide a uniform and precisely controlled low frequency acoustic measurement environment. The LFATC design is based on the original work of E.A.G. Shaw and G.J. Theisson at the National Research Council of Canada and a prototype LFATC developed by J.G. Ryan, E.A.G. Shaw, A. J. Brammer, and T.G. Zang. The design analysis of the LFATC is based both on a lumped parameter model and a one-dimensional standing wave model. The acoustic performance of this test cell, including a simple floor vibration isolation system, is evaluated experimentally over a wide range of sound pressure levels. A representative set of measurements with a prototype ANR headset illustrates the application of the LFATC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".