Cueing musical emotions: An empirical analysis of 24-piece sets by Bach and Chopin documents parallels with emotional speech
Bibliographic record
Abstract
Acoustic cues such as pitch height and timing are effective at communicating emotion in both music and speech. Numerous experiments altering musical passages have shown that higher and faster melodies generally sound "happier" than lower and slower melodies, findings consistent with corpus analyses of emotional speech. However, equivalent corpus analyses of complex time-varying cues in music are less common, due in part to the challenges of assembling an appropriate corpus. Here, we describe a novel, score-based exploration of the use of pitch height and timing in a set of "balanced" major and minor key compositions. Our analysis included all 24 Preludes and 24 Fugues from Bach's Well-Tempered Clavier (book 1), as well as all 24 of Chopin's Preludes for piano. These three sets are balanced with respect to both modality (major/minor) and key chroma ("A," "B," "C," etc.). Consistent with predictions derived from speech, we found major-key (nominally "happy") pieces to be two semitones higher in pitch height and 29% faster than minor-key (nominally "sad") pieces. This demonstrates that our balanced corpus of major and minor key pieces uses low-level acoustic cues for emotion in a manner consistent with speech. A series of post hoc analyses illustrate interesting trade-offs, with sets featuring greater emphasis on timing distinctions between modalities exhibiting the least pitch distinction, and vice-versa. We discuss these findings in the broader context of speech-music research, as well as recent scholarship exploring the historical evolution of cue use in Western music.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".