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Record W2569330962 · doi:10.1145/2930672

An Introduction to Musical Metacreation

2016· article· en· W2569330962 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

VenueComputers in entertainment · 2016
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputational creativityMusicalCreativityMusical compositionComputer scienceImprovisationTerminologyGenerative grammarContext (archaeology)Computer musicField (mathematics)Domain (mathematical analysis)Composition (language)Variety (cybernetics)New Interfaces for Musical ExpressionHuman–computer interactionCognitive scienceArtificial intelligenceVisual artsLinguisticsArtMathematicsPsychology

Abstract

fetched live from OpenAlex

Musical metacreation (MuMe), also known as musical computational creativity, is a subfield of computational creativity that focuses on endowing machines with the ability to achieve creative musical tasks, such as composition, interpretation, improvisation, accompaniment, mixing, etc. It covers all dimensions of the theory and practice of computational generative music systems, ranging from purely artistic approaches to purely scientific ones, inclusive of discourses relevant to this topic from the humanities. MuMe systems range from purely generative ones to a variety of interactive systems, such as those for computer-assisted composition and computer-assisted sound design. In order to better appreciate the many dimensions of this interdisciplinary domain and see how it overlaps and differs from research in computer music, this introduction provides a general entry point. After defining and introducing the domain, its context, and some of its terminology, we reflect on some challenges and opportunities for the field as a whole.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.713
Threshold uncertainty score0.281

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 science0.0010.000
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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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