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Record W2091106910 · doi:10.1525/mp.2004.22.2.207

Perception of Musical Similarity Among Contemporary Thematic Materials in Two Instrumentations

2004· article· en· W2091106910 on OpenAlexaff
Stephen McAdams, Sandrine Vieillard, Olivier Houix, Roger Reynolds

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2004
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsMelodyPianoTimbreRhythmPsychologySimilarity (geometry)Articulation (sociology)PerceptionPitch (Music)Movement (music)MusicalCommunicationPitch contourCognitive psychologyLinguisticsDynamics (music)Speech recognitionArtComputer scienceVisual artsArtificial intelligenceAesthetics

Abstract

fetched live from OpenAlex

Free classification was used to explore similarity relations in contemporary musical materials. Thirty-four subsections from the five themes of The Angel of Death by Roger Reynolds were composed identically for piano (Expt. 1) and chamber orchestra (Expt. 2) in terms of pitch, rhythm, and dynamics. Listeners were asked to group together those judged to be musically similar and to describe the similarities between the subsections in each group. Listeners based their classifications on surface similarities related to tempo, rhythmic and melodic texture, pitch register, melodic contour, and articulation. They were to some extent also based on similarity of the mood evoked by the excerpts. This latter factor was more prominent in the verbalizations for the orchestral version. Instrumentation, timbre, and type of timbral change (smooth, disjunctive) also affected classifications in the orchestral version. Perceptual relations among thematic materials within the piece and the interaction of form-bearing dimensions in musical similarity perception are discussed.

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.

How this classification was reachedexpand

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), 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 score0.967
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
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.077
GPT teacher head0.372
Teacher spread0.295 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2004
Admission routes1
Has abstractyes

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