The implementation of a soundscape approach and methodologies in an acoustic consulting context
Bibliographic record
Abstract
Acoustic engineering practice in the built environment increasingly aims to implement a soundscape approach as a base offering rather than supplementary to traditional noise control engineering. With the recent introduction of parts 1, 2, and 3 of ISO 12913, a suite of new resources has become available to apply a soundscape design approach within the built environment. There are various opportunities and obstacles associated with implementing the theory and standards as a built environment consultancy for clients on real world projects. This presentation discusses some examples and approaches adopted by Arup across the globe and highlights successes and barriers experienced along the way. Specific Arup project case studies of different scales are included where a shift towards adopting a soundscape design approach has been utilized in the broadest sense. Additionally, the role of acoustic consultants as a connection between theory, standards, and implementation is discussed.
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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 | 0.076 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".