Categorization of Typical and Atypical Combinations of Excitations and Resonators of Musical Instruments: Assimilation of the Unusual to the Familiar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sound categorization is automatic, yet very little is known about how this process works. Physical sound sources such as musical instruments generate sounds that carry timbral information about two mechanical components: The excitation sets into vibration the resonator, which acts as a filter to amplify, suppress, and radiate sound components. Given that excitation–resonator interactions are quite limited in the physical world, Modalys, a digital, physically inspired modeling platform, was utilized to simulate the combinations of three excitations (bowing, blowing, striking) and three resonators (string, air column, plate). This formed nine types of interactions, which are either typical (e.g., struck string) or atypical (e.g., blown plate). In two separate categorization tasks, participants chose either the excitation or resonator they thought produced each interaction. For the typical interactions, participants accurately categorized their excitations and resonators. Atypical interactions were assimilated to typical ones and listeners identified either the correct excitation or the correct resonator but not both. Hierarchical clustering revealed that interactions were perceived differently depending on the categorization task. These findings suggest that unfamiliar sound sources are interpreted as conforming to familiar sound sources for which mental models exist. These studies consequently exemplify the role of timbre in sound source recognition.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it