Playing the Field: A holistic approach to the analysis or electroacoustic music using higher order ambisonic technologies, computer vision, and the composer's collaborative environment
Bibliographic record
Abstract
The purpose of this thesis was to examine the acoustic space of contemporary, electronic art music. The goal was to gain a greater understanding of the sound field and the information that is retained when using higher order ambisonic (HOA) recording technology. In addition to HOA technology, I examine the musicological tools needed to effectively study this music. This includes genetic criticism, traditional theoretical and musicological approaches, sketch studies, and what I call the composer’s atelier. The knowledge gained from each of these different methodologies is collected and used to inform necessary adjustments to other analytical steps. Three simple examples were recorded using HOA technology and analyzed using the software that was developed. These preliminary studies of the sound field, while clearly showing the potential in the research, also show the current limitations of the technology. The main object of study is Touch, for piano and interactive electronics, by Canadian composer Keith Hamel. It is used as a representative example of the genre and uses multiple speakers for the diffusion of pre-recorded sounds, synthesis, and sounds manipulated in real-time. It also employs a novel gestural tracking method that allows the pianist to actively participate in the creation and manipulation of the electronic materials. I argue that it is necessary to understand not only musicological methodologies in order to analyse electronic music, but also to have a good understanding of the technologies used by the composer such as Max, PD, or NoteAbility Pro. Appreciating and understanding these tools on a technical level will allow for a more comprehensive analysis and understanding of the composition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".