Capturing orchestral music for three-dimensional audio playback
Bibliographic record
Abstract
This thesis details the design, implementation, and evaluation of a novel technique for orchestral music capture for three-dimensional audio reproduction.The technique is optimized for Japan Broadcasting Corp. (NHK)'s "22.2 Multichannel Sound" threedimensional audio playback format.The design of the technique draws upon previous research in spatial hearing, music recording for stereo and multichannel playback environments, concert hall acoustics, spatial impression in multichannel audio, and subjective evaluation and analysis of reproduced sound.Preliminary experiments investigate immersion and envelopment in three-dimensional music recording, as well as the relationship between microphone polar patterns and vertical height channel signal capture.A novel technique for three-dimensional orchestral music recording is then introduced.The technique is designed to capture a fully immersive sound scene featuring a cohesive orchestral image with realistic horizontal and vertical extent, stable sound source imaging, natural ensemble and scene depth, and a highly enveloping ambient sound field.A series of formal and informal subjective evaluations show that the proposed technique achieves these sonic imaging goals, and is suitable for 3D commercial music production and immersive content creation for broadcast.This new microphone technique is also applicable to other genres of music recording, as well as productions optimized for smaller-scale 3D audio formats.Further investigation finds 22.2 Multichannel Sound to be perceptually unique among common 3D audio formats with respect to the reproduction of acoustic music.A library of high-quality 3D audio test material was created for this research, which will be made available to other researchers for future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".