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Record W2166428112 · doi:10.5281/zenodo.1176986

Grassp: Gesturally-Realized Audio, Speech And Song Performance

2006· article· en· W2166428112 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceInterface (matter)GestureSpeech recognitionJitterMusical expressionAudio signal processingSound recording and reproductionSound (geography)Speech synthesisUser interfaceSpeech processingHuman–computer interactionMultimediaAudio signalMusicalSpeech codingArtificial intelligenceAcoustics

Abstract

fetched live from OpenAlex

We describe the implementation of an environment for Gesturally-Realized Audio, Speech and Song Performance (GRASSP), which includes a glove-based interface, a mapping/training interface, and a collection of Max/MSP/Jitter bpatchers that allow the user to improvise speech, song, sound synthesis, sound processing, sound localization, and video processing. The mapping/training interface provides a framework for performers to specify by example the mapping between gesture and sound or video controls. We demonstrate the effectiveness of the GRASSP environment for gestural control of musical expression by creating a gesture-to-voice system that is currently being used by performers.

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: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.321

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 communication0.0000.001
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 head0.007
GPT teacher head0.203
Teacher spread0.195 · 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

Quick stats

Citations23
Published2006
Admission routes1
Has abstractyes

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