Sounding the Inner Voice: Emotion and Vocal Emulation in Trumpet Performance and Pedagogy
Bibliographic record
Abstract
Sounding the Inner Voice: Emotion and Vocal Emulation in Trumpet Performance and Pedagogy Geoffrey Tiller Doctor of Musical Arts Faculty of Music University of Toronto 2015 Abstract This dissertation examines the aesthetics of trumpet performance with a focus on the relationship between a vocal approach and expressiveness in trumpet playing. It aims to improve current trumpet pedagogy by presenting different strategies for developing a theory of a vocal approach. Many of the concepts and anecdotes used in respected pedagogical publications and by trumpet teachers themselves are heavily influenced by the premise that emulating the voice is a desired outcome for the serious trumpet performer. Despite the abundance of references to the importance of playing trumpet with a vocal approach, there has been little formal inquiry into this subject and there is a need for more informed teaching strategies aimed at clarifying the concepts of vocal emulation. The study begins with an examination of vocal performance and pedagogy in order to provide an understanding of how emulating the voice came to be so central to contemporary notions of trumpet performance aesthetics in Western concert music. Chapter Two explores how emotion has been theorized in Western art music and the role that the human voice is thought to play in conveying such emotion. The final chapters develop a pedagogy of voice-like expression by integrating exercises based on Constantin Stanislavski's acting method, narrative analysis, and applied vocal practices to trumpet performance to strengthen the links between voice, emotion, and expressive musical communication.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".