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Record W2058002801 · doi:10.1017/s1355771802001024

Genres and techniques of soundscape composition as developed at Simon Fraser University

2002· article· en· W2058002801 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOrganised Sound · 2002
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSoundscapeComputer sciencePerspective (graphical)Sound (geography)Active listeningRepresentation (politics)AcousticsComposition (language)Range (aeronautics)Cognitive scienceVisual artsCommunicationArtificial intelligenceArtPsychologyEngineeringLiterature

Abstract

fetched live from OpenAlex

The soundscape composition, as pioneered at Simon Fraser University since the early 1970s, has evolved rapidly to explore a full range of approaches from the ‘found sound’ representation of acoustic environments through to the incorporation of highly abstracted sonic transformations. The structural approaches similarly range from being analogues of real-world experience, such as listening from a fixed spatial perspective or moving through a connected series of acoustic spaces, to those that mirror both nonlinear mental experiences of memory recall, dreams, and free association, as well as artificial sonic constructs made familiar and possible by modern ‘schizophonic’ audio techniques of sonic layering and embedding. The octophonic surround-sound playback format as used in contemporary soundscape presentations has achieved a remarkable sense of immersion in a recreated or imaginary sonic environment. Specific works realised at SFU are analysed that illustrate each of these approaches.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.208
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it