Spatial audio production for immersive media experiences: Perspectives on practice-led approaches to designing immersive audio content
Bibliographic record
Abstract
Sound design with the goal of immersion is not new. However, sound design for immersive media experiences (IMEs) utilizing spatial audio can still be considered a relatively new area of practice with less well-defined methods requiring a new and still emerging set of skills and tools. There is, at present, a lack of formal literature around the challenges introduced by this relatively new content form and the tools used to create it, and how these may differ from audio production for traditional media. This article, through the use of semi-structured interviews and an online questionnaire, looks to explore what audio practitioners view as defining features of IMEs, the challenges in creating audio content for IMEs and how current practices for traditional stereo productions are being adapted for use within 360 interactive soundfields. It also highlights potential direction for future research and technological development and the importance of practitioner involvement in research and development in ensuring future tools and technologies satisfy the current needs.
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 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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".