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Record W2145196605 · doi:10.1525/mp.2010.28.2.169

Melodic Contour Similarity Using Folk Melodies

2010· article· en· W2145196605 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

VenueMusic Perception An Interdisciplinary Journal · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsMelodySimilarity (geometry)Fourier analysisPitch contourFourier transformArtificial intelligenceCentralityComputer scienceSpeech recognitionPattern recognition (psychology)PsychologyCommunicationMathematicsStatisticsMathematical analysisArt

Abstract

fetched live from OpenAlex

Melodic contour, or the pattern of rises and falls in pitch, is a critical component of melodic structure, and has an important impact on listeners' perceptions of, and memory for, music. Despite its centrality, few formal models of contour structure exist. One recent exception involves characterizing contour by the relative degrees of strength of its cyclic information, quantified via a Fourier analysis of the pitch code of the contour. Three experiments explored the applicability of this approach, demonstrating that listeners' similarity ratings for pairs of melodies were predictable from Fourier analysis quantifications of rhythmically complex (Experiment 1) and rhythmically simple (Experiment 2) melodies, as well as for derived similarity measures based on melodic complexity judgments (Experiment 3). These findings indicate that Fourier analysis is an effective model of melodic contour, and that it can predict perceived melodic similarity.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score1.000

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 studies0.0030.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.083
GPT teacher head0.366
Teacher spread0.283 · 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