Familiarity of Western melodies: An exploratory approach to influences of national culture, genre and musical expertise
Bibliographic record
Abstract
It is unknown to what extent listeners in different Western countries share long-term representations of melodies as well as their genre associations, and whether such knowledge is modulated through music training. A group of German listeners ( N = 40) rated their familiarity with 144 melody excerpts from different genres implicitly (melody structure) and explicitly (melody title). Melodies were identical to those used in a previous Franco-Canadian study (Peretz, Babaï, Lussier, Hébert, & Gagnon, 1995). In addition, melodies were attributed by the participants to predefined genre categories, and similarities between pairs of melodies were computed, using an algorithm by Müllensiefen and Frieler (2006). Results revealed patterns of (un)familiarity, which, in part, deviated from the previous study. Melodies from classical, ceremonial, and – to a lesser extent – children’s songs categories were rated as most familiar, whereas traditional and more recent francophone tunes from mixed categories were judged as unfamiliar. Music training had no significant influence on implicit memory for melodies but rather on explicit knowledge of their titles. Computational analyses suggest that highly familiar and highly unfamiliar tunes share structural features with melodies belonging to the same category, whereas dissimilarities were detected between certain clusters of genre categories. Taken together, these results suggest that long-term representation of melodies is influenced by a listener’s (Western) national background. Representations are differently affected by specific genres but only partially influenced by music training and by structural properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".