SISA: Engaging with Performing Arts Through Spatialized Interaction with Segmented-audios in Immersive Environments
Bibliographic record
Abstract
Intangible cultural heritage (ICH) performing arts, particularly their auditory elements, face significant challenges in transmission and engagement among younger generations due to their inherently temporal and passive nature of appreciation. To address this challenge, we present a Spatial Interaction-based Segmented-Audio (SISA) system in Virtual Reality (VR) that transforms temporal auditory experiences into interactive spatial explorations. Our approach segments audio content and applies t-SNE algorithm for spatial clustering within VR environments. In this paper, we demonstrate SISA implementation through four VR scenes featuring two ICH genres - Peking Opera and Meshrep - each incorporating 5-second and 10-second audio segments. Through user testing with 16 participants, we explored users’ perceptions and interactions with SISA system within these VR environments. Our work advances technology-mediated cultural preservation by establishing a system and framework for converting temporal performances into navigable spatial experiences, creating new pathways for ICH engagement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".