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Record W1998828584 · doi:10.1017/s1355771814000260

Generative Music for Live Performance: Experiences with real-time notation

2014· article· en· W1998828584 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 · 2014
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsNotationGenerative grammarElectroacoustic musicMusical notationComputer scienceMusicalGenerative modelMusical compositionLinguisticsVisual artsArtArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Notation is the traditional method for composers to specify detailed relationships between musical events. However, the conventions under which the tradition evolved – controlled relationships between two or more human performers – were intended for situations apart from those found in electroacoustic music. Many composers of electroacoustic music have adopted the tradition for mixed media works that use live performers, and new customs have appeared that address issues in coordinating performers with electroacoustic elements. The author presents generative music as one method of avoiding the fixedness of tape music: coupled with real-time notation for live performers, generative music is described as a continuation of research into expressive performance within electroacoustic music by incorporating instrumentalists rather than synthetic output. Real-time score generation is described as a final goal of a generative system, and two recent works are presented as examples of the difficulties of real-time notation.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.437

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 studies0.0010.000
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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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