Spaced A-B placements of higher-order Ambisonics microphone arrays: Techniques for recording and balancing direct and ambient sound
Bibliographic record
Abstract
In acoustic music recording, spaced microphone positions typically give an impression of spaciousness and depth in the rendered sound scene. Inter-channel decorrelation, particularly at low frequencies, is thought to contribute to this effect, and cannot be achieved through coincident techniques. Higher order Ambisonics microphones have seen a rapid increase in popularity, despite their limitation as a coincident recording technique. In this article, recording and mixing strategies for the A-B spaced placement of HOA arrays are described combining the advantages of both: sound stage depth and stability, and the compatibility of b-format based mixing with respect to rendering for binaural and immersive 3D reproduction loudspeaker systems. Two tests were conducted to evaluate a) the suitability of various A-B spacings with regards to direct and/or ambient sound, and b) a novel two-dimensional mixing control tool, to study direct and ambient sound optimization. Results show that increased A/B spacing of HOA receivers correlates to more optimal presentation of ambient sound. Furthermore, users interacted with the mixing control tool differently when asked to optimize the sound scene for either direct or ambient sound, or both, providing support for our microphone placement and mixing techniques. This work points to new directions for the integration of HOA microphones with traditional recording techniques towards improved spatial representation of mediated music.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".