Comprehensive Framework for Describing Interactive Sound Installations: Highlighting Trends through a Systematic Review
Bibliographic record
Abstract
We report on a conceptual framework for describing interactive sound installations from three complementary perspectives: artistic intention, interaction and system design. Its elaboration was informed by a systematic review of 181 peer-reviewed publications retrieved from the Scopus database, which describe 195 interactive sound installations. The resulting taxonomy is based on the comparison of the different facets of the installations reported in the literature and on existing frameworks, and it was used to characterize all publications. A visualization tool was developed to explore the different facets and identify trends and gaps in the literature. The main findings are presented in terms of bibliometric analysis, and from the three perspectives considered. Various trends were derived from the database, among which we found that interactive sound installations are of prominent interest in the field of computer science. Furthermore, most installations described in the corpus consist of prototypes or belong to exhibitions, output two sensory modalities and include three or more sound sources. Beyond the trends, this review highlights a wide range of practices and a great variety of approaches to the design of interactive sound installations.
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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.096 | 0.076 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".