Bibliographic record
Abstract
This paper outlines the requirements for an interchange format that can describe and share spatial parameters across 3D audio applications, and proposes SpatDIF for its implementation. 1. WHY USING A SCENE DESCRIPTION FORMAT FOR COMPOSING? Formats as a structuring concept are integral to musical practice. For example, in the form of scores, a written symbolic representation of music, compositions can be stored, exchanged, studied, performed but also revised and adapted after their initial creation. MusicXML shows how the score concept is digitally maintained. Although spatialization can be considered as a core element of electroacoustic music, there is no general consensus in how to describe and notate spatialization. Nowadays the spatial aspects are mostly created and automatized on a low-level within diverse digital audio environments, such as Max/MSP or ProTools. Because these environments have different syntaxes, units, and storage solutions, the control messages (e.g. a trajectory to move a sound in space) are only valid within this specific audio environment. Therefore the interchangeability of these descriptors is ineffectual and usually spatial aspects are directly rendered into multichannel sound files. Now that processing power is usually sufficient to render multiple virtual sound sources in real-time, a separation of “raw” sound material from the spatial descriptors within an open data format would increase the portability across different 3D audio applications, loudspeaker configurations and concert venues. Furthermore, spatial rendering algorithms could be compared and combined without having to change the spatial-sound syntax. This, of course, relies on the standardization of descriptors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".