Bowed air columns and blown plates: Odd combinations of excitation mechanisms and resonant structures impact perception
Bibliographic record
Abstract
The current study examines how interactions between excitation mechanisms and resonant structures influence the perception of these mechanical components. Nine interactions between three excitations (bowing, blowing, striking) and three resonators (string, air column, plate) were simulated with Modalys, a digital physically inspired modeling platform. Interactions were either typical (e.g., bowed string) or atypical (e.g., bowed air column) of acoustic musical instruments. Two experiments were conducted using three exemplars of each interaction. Exemplars were created by manipulating two parameters that affect the resulting timbres for each excitation type. Two groups of listeners rated how well the exemplars resembled each excitation type (Experiment 1) or each resonator type (Experiment 2). Listeners assigned the highest resemblance ratings to the correct excitations and resonators that produced the typical interactions. For the atypical interactions, listeners assigned the highest resemblance ratings to either the correct excitation or resonator but not both. The mechanical component they correctly perceived biased perception toward the complementary component that it most typically interacts with in acoustic musical instruments. Mental models for sound source components seem to be limited by listeners’ familiarity with them.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".