Intelligent Musical Instruments: Challenges for the Composer/Performer
Bibliographic record
Abstract
The field called New Interfaces for Musical Expression (NIME) has arisen to address questions of Human-Computer Interaction in the context of musical performance.The interface is a gesture sensor whose output is mapped by software to actuate sound generators. As we introduce more and more complex software to interpret and mediate the musical gestures of the performer, it may become more difficult for the observer to interpret what the performer is doing. Thus a guiding principle of our research is to retain expressive musical interaction with NIME when making software and hardware design decisions. The research outcome is a musical performance where the observer perceives an intuitive relationship between the musical gestures and the musical sounds, even though the observer may not discern the exact mapping between gesture and sound. We demonstrate some musical applications for our research, following the development of the radiodruminterface and the Mechdrum sound generator.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".