The Feasibility of Statistical Energy Analysis in the Modeling of Stringed Musical Instruments
Bibliographic record
Abstract
Stringed musical instruments are complex vibrating systems both from structural and fluid-structure coupling perspectives; hence, their modeling is one of the most challenging tasks in the area of vibration and acoustics. Making a reliable model not only broadens our knowledge of the physics of these instruments, but also it simplifies the procedure of structural modification and optimization on them. In this regard, a Finite Element Model has been previously made from Setar and is verified with the experimental results. Although that model could precisely simulate the instrument in lower frequencies (i.e. below 2.5 KHz), its results showed a weak correlation with reality in higher frequencies. In fact, unreliable results and high computational demand are common drawbacks of finite element method in higher frequencies. To avoid these problems, in this study Setar is modeled with Statistical Energy Analysis (SEA) approach. This method is more efficient in dealing with high degree of uncertainty in the system. SEA does this by averaging the response over the frequency and location to gain a more general and reliable result. Application of SEA in higher frequencies is, in fact, compatible with the nature of musical instruments where in higher frequencies we are mostly interested in the trend of the response rather than the location of each individual peak.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".