Bibliographic record
Abstract
The estimation of fundamental frequency of instruments is an important task in computational audio analysis. The current state of the art methods use neural networks for this task. This process is typically computed periodically over very short segments of a monophonic audio signal so that minute shifts in intonation can be detected. However, the steelpan has discretely tuned notes where the performer has no direct control over pitch once a note has been activated. The activation of a note has great influence over the acoustical properties of the resultant note. Much research has been devoted to the tonality, construction, and acoustical properties of steelpans, but relatively little focuses on the attack transient specifically.This paper evaluates the application of pitch detection methods to the attack transients of steelpan notes. A dataset containing labeled audio samples from multiple tenor steelpans is used for training and evaluation. The accuracy of this approach for pitch detection is compared with established methods applied to both entire notes and only attack transients. Determining a steelpan note’s pitch from the attack transient is an important first step in building a robust low latency automatic transcription system that can be used for both analysis as well as live performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".