Maintenance strategies and design recommendations on input devices for musical expression
Bibliographic record
Abstract
Various challenges must be overcome when designing and building input devices for musical expression such as digital musical instruments (DMIs), electronic wearables for performance, among others.Amid the technical ones are the rapid unpredictable advancement of technology, changes in supporting personnel, and a lack of technical documentation.These challenges, if not adequately addressed, in many cases lead to abandonment of such devices and endeavors.This thesis presents three case studies that aim to provide maintenance strategies and design recommendations for the creation of more robust, long-lasting interfaces.The first case study illustrates the challenge of maintaining several models of the DMI called the T-Stick in the hopes of extending their useful lifetime.The T-Sticks were originally conceived in 2006 and 20 copies have been built in the past 12 years.Although all of the instruments preserve the original DMI design concept, their evolution has distinguished them through variations in choice of microcontrollers, sensors, and size.For this case study, we worked with eight copies of the T-Sticks to overcome issues related to the aging and obsolescence of components, changes in external software, inconsistencies in firmware across versions, a lack of documentation, and, in general, the problem of technical maintenance.In the second case study, we redesigned the electronics of a ninth T-Stick, the WiFi Sopranino.For this process we used the lessons learned from the first case study, while keeping its original DMI design concept.This work, in turn, informed the third case study, where we sought to design and build a new interface for musical expression to augment cello performance by installing a visual affordance on the instrument.In short, this thesis aims to connect the concepts of technical maintenance and electronic redesign and design to provide a systematic approach to the maintenance of these type of devices.We articulate design recommendations such as maintainability, reusability, and self-containment as useful in the design of input devices for musical expression.Many thanks to all the great people at the IDMIL and CIRMMT.Thanks to my friends all over the world who contributed to this " trippy que me sube".Thanks to Pocotierno for rockin' the house.To my mom, for putting me in this world.Without you, I wouldn't be what I am.And the rest of
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".