Graphical Performance Software in Contexts: Explorations with Different Strokes
Bibliographic record
Abstract
This thesis proposes a novel approach to musical software analysis that prescribes testing a given software interface in a wide variety of hardware contexts, each providing unique insights into its design. This work is situated in the general context of graphical software performance, which we define as musical performance through manipulating an on-screen software interface to create music. The analysis strategy is investigated using the Different Strokes (DS) performance environment as a specific example. A series of software extensions to DS were undertaken to extend the application to new hardware contexts and use cases. These include extensions for the use of Different Strokes in an interdisciplinary performance work, d_verse; the adaptation of DS to work on a large multi-touch surface; the integration of a force-feedback input device; and the integration of the libmapper framework, allowing it to be easily interconnected with alternative input and output devices. The thesis also presents a historical overview of graphical software intended for live use, and a background on general issues in interface design for this usage context. An exploratory user test was performed with the force-feedback setup where participants used DS in the presence of simulated physical forces. While there was no clear preference for any of the haptic effects, the different physical forces present are demonstrated to have gestural implications. These kinds of implications should be taken into account when designing mappings from gesture to sound, and in the overall interaction design.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".