The Hyper Drumhead: Making Music with a Massive Real-time Physical Model
Bibliographic record
Abstract
Physical Modelling (PM) techniques based on numerical analysis allow for the creation of precise and diverse models.Although widely employed in non-real time acoustic simulations of real systems, this approach is rarely used for the design of models for Digital Musical Instruments (DMIs).This is due to limitations in terms of computational load, modularity and interactivity.In this paper, we present the Hyper Drumhead, a novel percussive DMI that leverages the precision of numerical analysis-based PM, while providing modular and expressive controls.This is achieved by means of a Finite-difference Time-domain solver, whose novel implementation takes advantage of the parallel computing capabilities of commodity graphics cards (GPUs).We first present the main details of our GPU implementation, then we describe how the Hyper Drumhead takes advantage from its distinctive features, in the context of musical expression.Finally, we perform a preliminary evaluation of the efficiency and of the modularity of the model, by means of assessing the performance of the instrument.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".