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Record W2139879587 · doi:10.1109/jproc.2004.825887

Designing for Intimacy: Creating New Interfaces for Musical Expression

2004· article· en· W2139879587 on OpenAlexaff
Sidney Fels

Bibliographic record

VenueProceedings of the IEEE · 2004
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMusical expressionMusicalExpression (computer science)GestureHuman–computer interactionComputer scienceNew Interfaces for Musical ExpressionSound (geography)Musical instrumentAestheticsMusical compositionArtAcousticsVisual artsArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

Contemporary musical instrument design using computers provides nearly limitless potential for designing the mapping between gesture and sound. When designing effective and expressive musical instruments, the types of relationship between musician/player and his instrument and the aesthetics of the relationships must be considered. This paper discusses four types of relationships and their aesthetics. A high degree of intimacy is achieved when the relationship reaches a level where the mapping between control and sound is transparent to the player, that is, the player embodies the device. Ultimately, this type of relationship allows intent and expression to flow through the player to the sound and, hence, create music. Three new interfaces for musical expression, the Iamascope, Sound Sculpting and Tooka, provide examples of how instruments may be designed to develop and explore intimacy and embodiment of new musical instruments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0050.009
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.029
GPT teacher head0.266
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations64
Published2004
Admission routes1
Has abstractyes

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