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Record W2914859903 · doi:10.1121/1.5087277

A compact pitch and time representation for melodic contours in Indian art music

2019· article· en· W2914859903 on OpenAlex
H. G. Ranjani, Ajay Srinivasamurthy, Deepak Paramashivan, T.V. Sreenivas

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

VenueThe Journal of the Acoustical Society of America · 2019
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMelodyRhythmPitch contourRepresentation (politics)Speech recognitionComputer scienceInterval (graph theory)Quantization (signal processing)MathematicsArtificial intelligenceAcousticsComputer visionMusicalPhysicsArt

Abstract

fetched live from OpenAlex

Predominant melody offers a complete representation of melodic contours of heterophonic Indian Art Music (IAM). A compact representation of melodic contours while preserving raga characteristics is proposed. Such representations have applications in music transcription, analysis, and synthesis. Contours are quantized on a pitch-time grid after normalizing critical points with tonic and rhythmic pulse period estimates. Non-uniform quantization intervals are selected from pitch and time scales prevalent in IAM, while accommodating pitch and inter-note-interval variations on pitch-time grid. An evaluation of quantized-reconstructed contours through listening tests by trained musicians shows raga preserving capabilities of the proposed approach in spite of alterations in contour shapes.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.152

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 communication0.0000.000
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 head0.015
GPT teacher head0.262
Teacher spread0.247 · 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