Refining the stereo technique for augmented ambience gradient: Improvements in stereo image, spatial envelopment, and mixing flexibility
Bibliographic record
Abstract
While working on location, recording engineers are often challenged by insufficient monitoring capabilities. Poor (temporary control room) acoustics and/or mandatory headphone monitoring can make judgments regarding microphone choice and placement difficult. This compromised monitoring often leads to timbral, phase, and stereo image problems. We are often forced to compromise between the improved spatial imaging of near-coincident techniques and the attractive acoustic envelopment of spaced omnidirectional mics. This research reviews a new technique: Stereo Technique for Augmented Ambience Gradient (STAAG), which aims to improve stereo imaging, ambient envelopment, and flexibility in the mix. Building on a preliminary study, this research realizes ideal microphone angle/spacing combinations to promote spatial accuracy. In addition, it investigates the quality of the ambient envelopment compared to omnidirectional-based techniques, and the ability of STAAG to allow an engineer to manipulate the direct to reverberant energy ratio during post production without corrupting the stability of the stereo image.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".