Soundscape Composition as Global Music: Electroacoustic music as soundscape
Bibliographic record
Abstract
Abstract The author covers the background of soundscape composition, as initiated by the World Soundscape Project at Simon Fraser University, and soundscape documentation as an activity that is being increasingly practised worldwide. Today there are two striking manifestations of this work: the increasing globalisation of the electroacoustic community, and the increasing sophistication of digital techniques applied to soundscape composition. In addition, the tradition of listening to environmental soundscapes as if they were music is inverted to suggest listening to electroacoustic music as if it were soundscape. What analytical tools and insights would result? The theoretical concepts introduced in soundscape studies and acoustic communication are summarised and applied first to media and digital gaming environments, noting the extensions of both their sound worlds and the related listening attitudes they provoke in terms of analytical and distracted listening. Traditional approaches to acousmatic and soundscape analysis are compared for their commonalities and differences, the latter being mainly their relative balance of attention towards inner and outer complexity. The types of electroacoustic music most amenable to a soundscape based analysis are suggested, along with brief examples of pieces to which such analysis might be directed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".