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Record W2033237937 · doi:10.1145/1841317.1841320

Ancient Chinese zither (guqin) music recovery with support vector machine

2010· article· en· W2033237937 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal on Computing and Cultural Heritage · 2010
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsUniversity of WaterlooWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNotationDuration (music)MemorizationComputer scienceMusical notationMusicologySpeech recognitionNatural language processingVisual artsMusicalLiteratureArtCognitive psychologyPsychologyLinguistics

Abstract

fetched live from OpenAlex

The Chinese zither, called guqin, has existed for over 3,000 years and always played an important role in Chinese social history. An interesting but unfortunate fact is that the traditional notation of guqin music does not provide the duration information for each music note which requires the player to learn from his teacher and memorize. As a result, among several thousands of compositions that have been created and recorded with guqin music notation, only around 100 of them are still being played today. In this article we use a machine learning method to study the guqin music recovery problem which tries to use the guqin music notation to recover the duration of each music note. Information provided by the music note is used as features to predict the duration information with a support vector machine. The experimental result shows that our system can predict with fair accuracy, and can be used as a valuable reference for human guqin masters to recover guqin music.

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 score0.868
Threshold uncertainty score0.704

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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