Emotional communication in music: Implications for understanding links between between speech and music
Bibliographic record
Abstract
Parallels in the structure of speech and music have long fascinated great thinkers from Plato to Darwin. The communication of emotion is no exception, as it touches on one of the primary motivations for musical listening – feeling “moved by the music.” Although cues such as pitch height and timing are widely recognized as playing an important role in both domains, their role in music has been less researched than in language. Here I will discuss several of my team’s explorations aimed at providing a musical complement to existing work in speech and linguistics. The plethora of speech corpora provide useful data for examining the “natural” use of acoustic communicate emotion. Unfortunately, the clear contrast between the number of ‘effective speakers’ and the number of ‘effective composers’ complicates efforts to fully explore parallels in structural cues between these domains. Numerous studies based on manipulations of simplified musical stimuli such as newly composed single line melodies suggest important parallels between the communication of emotion in speech and music. Melodies transposed higher in pitch sound “happier” and melodies played at slower tempi sound “sadder” – paralleling the use of these cues in speech. However, it is not clear whether these simplified approaches capture the nuanced ways in which great composers employ such cues in their writing. Here I will discuss my team’s research on this important issue, which mixes the techniques of acoustical analysis, psychophysical testing, data visualization, and empirical musicology to provide a diverse exploration of musical emotion complementing and extending speech studies. In doing so my team aims to deepen our understanding of mechanisms shared between these ubiquitous human activities, providing insights useful to linguistics, musicians, psychologists, and cognitive scientists alike.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".