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Record W2396713126

Cross-Modal Parametric Composition

2004· article· en· W2396713126 on OpenAlexaff
David Gerhard, Daryl H. Hepting

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsComputer scienceComposition (language)Representation (politics)Parametric statisticsModalSet (abstract data type)Space (punctuation)Iterative and incremental developmentProcess (computing)Artificial intelligenceTheoretical computer scienceMathematicsProgramming languageArt
DOInot available

Abstract

fetched live from OpenAlex

We propose an interactive composition technique to facilitate the exploration of complex parameter spaces. More computeraided composition than algorithmic composition, human aesthetic judgment is an integral part of the technique. From a set of central parameters, the composer chooses a mapping which allows the generation of both audio and video representations. Features from each representation are then extracted and used as parameters in an iterative re-render process, each step of which can be automated or respond to human input. With this technique, composers can see as well as hear their music as it develops, and can interact and follow paths through the parameter space, exploring sound spaces heretofore unavailable to standard compositional methods.

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.

How this classification was reachedexpand

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.571
Threshold uncertainty score0.173

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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2004
Admission routes1
Has abstractyes

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