The creation of hardware systems for professional artistic productions
Bibliographic record
Abstract
This dissertation examines the challenges of creating digital musical instruments (DMIs) and other hardware and software systems that are intended for use in professional artistic productions.While the design of any DMI is not a trivial task, moving new instruments to the professional concert stage presents an additional set of challenges that encompass issues of technical design, use in artistic practice, manufacturing, and longterm usability.Although DMI designers often describe these challenges in accounts of their practice, existing overviews of the DMI design process primarily remain focused on issues of device functionality.To address this gap, I present a framework consisting of seven design aspects: functionality, aesthetics, support for artistic creation, system architecture, manufacturing, robustness, and reusability.Each aspect presents a different perspective on the challenges of designing DMIs for professional artistic productions, and requires the establishment of a set design requirements to meet these challenges.In practice, the design requirements of different aspects will often conflict, and creating solutions to solve these conflicts is essential to the design's success.The creation of this framework draws upon my experience in the creation of three hardware systems: The Prosthetic Instruments, the Ilinx garment, and the Vibropixels.For each system, a technical description and description of use will be presented, as well as a discussion highlighting the role of the design aspects in the system's development.Finally, a set of design principles is presented that address individual design aspects.These principles reflect general design goals intended to assist in the creation of DMIs for use in professional artistic productions.1 See Chadabe (1996) for an overview of the development of electronic and digital musical instruments. 2Which originated as a workshop at the 2001 Conference on Human Factors in Computing Systems (CHI).3 The abbreviation NIME is very commonly used to refer to the conference and research area in addition to being used as a noun synonymous with digital musical instrument.
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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 | 0.001 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".