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Record W196455693

POLYPHONIC EMBOUCHURE ON AN INTRICATELY EXPRESSIVE MUSICAL KEYBOARD FORMED BY AN ARRAY OF WATER JETS

2009· article· en· W196455693 on OpenAlexaff
Steve Mann, Ryan Janzen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolyphonyMusicalComputer scienceKey (lock)Context (archaeology)Human–computer interactionComputer musicJet (fluid)Musical instrumentAcousticsPianoViolinInterface (matter)Electroacoustic musicSpeech recognitionPhysicsEngineeringAerospace engineeringArt
DOInot available

Abstract

fetched live from OpenAlex

Touching, diverting, restricting, or obstructing water jets constitutes a new type of user-interface for immersive multimedia environments such as totally acoustic, totally electronic, or hybrid musical instruments. The result is a richly expressive input device. In the context of a musical instrument, this device is called a hydraulophone. Developments in the rich expressivity of the hydraulophone are presented, as a new type of embouchure control. This paper presents a new concept called “finger-jet embouchure” in which each “key ” (water jet) on the instrument is governed by fluid-dynamics, rather than by solid key motion. The direct coupling between a musician’s finger and physical sound production in the liquid (which can be detected by underwater microphones and fed into a computer, thus creating a hyperacoustic user-interface) leads to highly expressive performance styles. By designing each note to be associated with one water-jet “mouth ” on the instrument, we give the performer an ability to fluidly interleave the dynamics of many notes simultaneously, producing a phenomenon named “polyphonic embouchure”. The result is the ability to perform richly expressive music, whether on an acoustic hydraulophone, or on an enhanced hydraulophone with sound pickups for hyperacoustic computer performances. 1.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.438

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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 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

Citations3
Published2009
Admission routes1
Has abstractyes

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