A framework and tools for mapping of digital musical instruments
Bibliographic record
Abstract
Digital musical instruments (DMIs) are typically composed of an interface using some type of sensor technology, and real-time media synthesis algorithms running on a digital computer.The connections between various input signals from performer interaction and the parameters of synthesis must be artificially associated -this mapping of gesture to sound or other media defines the behaviour of the system as a whole.Mapping design is a challenging and sometimes frustrating process.In this dissertation, the design and implementation of an open-source, cross-platform software library and several related tools for supporting the mapping task are presented.These tools are designed to provide discovery and interconnection between parts of DMIs and other interactive systems, and to achieve compatibility through translation and transformation of data representations rather than imposing representation standards.The control parameters of software and hardware devices compliant with libmapper can be freely interconnected without requiring any intended mutual compatibility.Among the unique features presented is support for mapping between systems that include entities with multiple instances with dynamic lifetimes, systems which would usually require bespoke programming.A formalization of the problem is described, and several examples of real-world applications are outlined.Finally, two use-cases for the mapping tools are presented in-depth: the development of the T-Stick digital musical instrument, and the design and use of prosthetic musical instruments for interactive dance/music performance.Extra thanks go to Stephen Sinclair as co-creator of libmapper, not only for his own brilliance and hard work, but for being such a patient resource.Many others have also contributed to the library and surrounding toolset: Mark Zadel for the SuperCollider language bindings, Vijay Rudraraju, Aaron Krajeski, and Jonathan Wilansky on graphical user interfaces, Jérome Nika and Gautam Bhattacharya on some core functionality, Mahtab Ghamsari-Esfahani, Avrum Hollinger and Vanessa Yaremchuk on machine learning tools.Thanks also to D. Andrew Stewart for tireless beta-testing.Extra
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".