SPATOSC: PROVIDING ABSTRACTION FOR THE AUTHORING OF INTERACTIVE SPATIAL SUDIO EXPERIENCES
Bibliographic record
Abstract
Creating an interactive project that deals with 3-D sound becomes difficult when it needs to run in multiple venues, due to a diversity of potential loudspeaker arrangements and spatialization systems. For effective exchange, there needs to be a standardized approach to audio spatialization with a unified, but abstract way of representing 3D audio scenes. Formats from the gaming and multimedia communities are appealing, but generally lack the features needed by artists working with interactive new media. Non-standard features need to be supported, including support for multiple listeners, complex sound directivity, integration with networked show control and sound synthesis applications, as well as support for the tuning spatial effects such as Doppler shift, distance attenuation and filtering. Our proposed solution is an open source C++ library called SpatOSC,1 which can be included in existing visualization engines, audio development environments, and plugins for digital audio workstations. Instead of expecting that everyone adopt a new spatial audio format, our library provides an immediate solution, with “translators” that handle conversion between representations. A number of translators are already supported, with an extensible architecture that allows others to be developed as needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".