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Record W2098282310 · doi:10.1109/icme.2008.4607378

Hyperacoustic instruments: Computer-controlled instruments that are not electrophones

2008· article· en· W2098282310 on OpenAlexaff
Steve Mann, Ryan Janzen, Raymond Lo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMIDIMusical instrumentComputer scienceMusicalViolinAcousticsProcess (computing)Sound (geography)Human–computer interactionVisual artsArt

Abstract

fetched live from OpenAlex

This paper describes a musical instrument consisting of a physical process that acoustically generates sound from the material world (i.e. sound derived from matter such as solid, liquid, gas, or plasma) which is modified by a secondary input from the informatic world. This informatic input selects attributes such as the frequency range of the musical note being sounded, while the acoustic process is kept in close contact with the user, to ensure a high degree of expressivity. In one example, ice skates with acoustic pickups are used to play music while the skater simultaneously controls a bandpass filter with a hand-held keyer and wearable computer. Each skate works much like the bow on a violin, allowing the player to hit, scrape, rub, or "bow", the ice in various ways to create a wide variety of musical textures. Additionally the player can select sound samples on a per-note basis and then "scratch" out a melody or harmony (playing multiple samples at once) on the ice on the rink like a team of disk jockeys (DJs) working together to "scratch" an array of vinyl records. Because the grooves on an ice rink are made by the player in a freeform fashion, there is much more room for variations in musical timbres and textures than with the fixed grooves of a record. Rather than merely using the keyer to trigger musical notes through MIDI note on/note off commands, we create acoustic sound through a physical process such as skating, and then turn those physical sounds into musical notes with the handheld keyer that functions as a modifier input. This combination combines the expressivity of non-electrophonic musical instruments like the violin with the flexibility of electrophones like the sound synthesizer. As a further contribution of the paper, a general taxonomy of acoustic transducers and a link to physical organology is provided, in which the top-level of the taxonomy is the state-of-matter in which the transducer operates.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.019

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 head0.021
GPT teacher head0.215
Teacher spread0.194 · 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 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".

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Citations5
Published2008
Admission routes1
Has abstractyes

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