Extended Playing Techniques on an Augmented Virtual Percussion Instrument
Bibliographic record
Abstract
Abstract Innovation and tradition are two fundamental factors in the design of new digital musical instruments. Although apparently mutually exclusive, novelty does not imply a total disconnection from what we have inherited from hundreds of years of traditional design, and the balance of these two factors often determines the overall quality of an instrument. Inspired by this rationale, in this article we introduce the Hyper Drumhead, a novel augmented virtual instrument whose design is deeply rooted in traditional musical paradigms, yet aimed at the exploration of unprecedented sounds and control. In the first part of the article we analyze the concepts of designing an augmented virtual instrument, explaining their connection with the practice of augmenting traditional instruments. Then we describe the design of the Hyper Drumhead in detail, focusing on its innovative physical modeling implementation. The finite-difference time-domain solver that we use runs on the parallel cores of a commercially available graphics card and permits the simulation of real-time 2-D wave propagation in massively sized domains. Thanks to the modularity of this implementation, musicians can create several 2-D virtual percussive instruments that support realistic playing techniques but whose affordances can be enhanced beyond most of the limits of traditional augmentation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".