Using soundscape simulation to evaluate compositions for a public space sound installation
Bibliographic record
Abstract
While urban sound management often focuses on sound as a nuisance, soundscape research suggests that proactive design approaches involving sound art installations can enhance public space experience. Nevertheless, there is no consensus on a methodology to inform the composition of sound installations through soundscape evaluation, and little research on the effect of composition strategies on soundscape evaluation. The present study is part of a research-creation collaboration around the design of a permanent sound installation in an urban public space in Paris (Niches Acoustiques by Nadine Schütz). We report on a laboratory study involving the evaluation of composition sketches prior to the deployment of the installation on-site. Participants familiar with the public space (N = 20) were exposed to Higher-Order Ambisonics recordings of the site, to which compositions of the sound installation pertaining to different composition strategies were added using a soundscape simulation tool. We found three principal components relevant for evaluating and comparing sound installation sketches: pleasantness, familiarity and variety. Further, all composition sketches had a significant effect on the soundscape's familiarity and variety, and the effect of the compositions on these two components was stronger when composition strategies involved abstract sounds (sounds which were not clearly identifiable).
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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".