Scenophony: An undisciplinary approach to the design of immersive sound storytelling
Bibliographic record
Abstract
Based on established artistic practices focusing on spatial audio in the context of immersive sound experiences, we bring forth and discuss the concept of scenophony – following Gilles Malatray’s description of the word ‘scénophonie’. Derived from the term scenography, scenophony refers to the practice of the (scenic) space, primarily considering its acoustic situation (phonic) as perceived by the audience. Scenophony is the design and realization of the sound space as a whole: a perception-led creative practice at the intersection of sound design, audio art, musical composition, acoustics, psychoacoustics, audio engineering, and user experience design. The main object of this article is to define the role of scenophony and its involvement in the creation and overall reception of a sonic experience within or as a scenic space, as well as its potential applications and benefits in the context of immersive storytelling. Moreover, we propose a paradigm shift from the common representations used in most current sound spatialization tools, generally assuming a single, ideal, egocentric, sweet-spot-oriented approach – mostly aimed at VR/AR/MR applications – as evidenced by both the graphical (UI) representation of Euclidean space as well as the preferred, often single, audio spatialization technique proposed. The hypothesis is that such egocentric graphical representations of some sound spatialization tools may limit how sound and music are diffused, as well as affect how sonic scenes are designed and listened to. In contrast to such representations, especially in the context of collective and heterogeneous sound experiences, we propose that an allocentric approach to spatial sound design, grounded in the concept of scenophony, offers new ways to create stories through immersive sound.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".