Between Speech and Music: Composing for Guitar with Dialectal Patterns
Bibliographic record
Abstract
Music and speech share many of the same fundamental properties, with music often being referred to as a "universal language".The 20 th century saw many composers using speech as a musical element, at times combining it with acoustic instruments and exploiting the sonic similarities.Given the wide variety of timbres and techniques available on both the classical and electric guitar, it has proven to be an effective vehicle to imitate and blend with the human voice in speech-inspired compositions.This project examines the methods with which composers applied these concepts to contemporary guitar music, as well as the potential they yield to invoke a sense of place, nostalgia, and meaning in the audience.The core applications of this research involved a collaborative research-creation project with composer, Jason Noble, in which we created three new speech-based works involving guitar and electronics.In 2015, we decided to tour our home province of Newfoundland to record interviews with residents, and then to use those recordings as source material for musical creation.The dialects of Newfoundland and Labrador are diverse yet diminishing; and so we sought to celebrate them in an artistic context through this project.This paper discusses the creative and technological processes behind Noble's works One Foot in the Past (2016), Take Me Back (2017), and We Never Told Nobody (2019), which collectively celebrate the dialects of Newfoundland and Labrador while simultaneously contributing to the fields of guitar technique, notation, and composition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".