Reproducibility of speech intelligibility scores in spatially reconstructed and re-reconstructed vehicle cabin noise
Bibliographic record
Abstract
We assessed the fidelity of the multichannel freefield soundfield reconstruction technique proposed by Minnaar et al (AES 2013) by comparing Hearing in Noise Test (HINT) speech reception thresholds (SRTs) obtained in reconstructed, and then re-reconstructed, vehicle cabin noise soundfields. Recordings were first made in a moving vehicle under a variety of driving conditions (50 km/hr, smooth/rough road, open/closed windows) using a 32-channel spherical microphone array (Eigenmike ®, m.h. acoustics) placed in the passenger's head location. Using a six-loudspeaker array in the same stationary vehicle, the soundfields were approximately reconstructed, and SRTs were obtained for 12 normally hearing listeners located in the passenger seat; a small loudspeaker reproduced target speech from the driver's position and two rear passenger positions. For comparison, the in-vehicle reconstructed soundfields (R1) and speech were re-recorded and then re-reconstructed (R2) in an anechoic chamber using a 48-loudspeaker array, where SRTs were obtained for the same listeners. Mean SRTs were uniformly lower in R2 by 0.32 to 1.87 dB depending on speaker location and driving condition. The differences were larger when the speaker was beside rather than behind the listener by a mean of 0.98 dB and larger when windows were open by a mean of 0.23 dB. The results suggest that the reconstruction technique requires minor refinement in order to yield speech intelligibility scores equivalent to those in the original environment.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".