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Record W2086341330 · doi:10.1080/09298210500280554

Preservation and extension of traditional techniques:Digitizing North Indian performance

2005· article· en· W2086341330 on OpenAlex
Ajay Kapur, Philip Davidson, Perry R. Cook, W. Andrew Schloss, Peter F. Driessen

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

VenueJournal of New Music Research · 2005
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLaptopComputer scienceDrumGestureMovement (music)Human–computer interactionSonificationExtension (predicate logic)Meaning (existential)MultimediaComputer graphics (images)Artificial intelligenceEngineeringArtAestheticsProgramming language

Abstract

fetched live from OpenAlex

Abstract This article describes systems for capturing gestures from a performing artist playing North Indian instruments. Modified traditional instruments use sensor technology and microcontrollers to digitize performance, enabling a computer to synthesize sound and generate visual meaning. Specifically, systems were built to capture data from three traditional North Indian instruments: the tabla (a pair of tonal hand drums), the dholak (a barrel-shaped folk drum played by two people) and the sitar (a 19-stringed, gourd-shelled instrument). The article discusses how these instruments are modified to capture gestural movement, how these signals are mapped to sounds and graphical feedback, and gives examples of the new instruments being used in live performance. Modified performance techniques with the aid of a laptop computer are introduced; however, the hardware is built to try and preserve the techniques passed down from generations of tradition.

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)0.001
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 score0.925
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.192
GPT teacher head0.339
Teacher spread0.147 · 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