Improved steelpan pitch detection through audio feature extraction and machine learning
Bibliographic record
Abstract
The steelpan, a significant musical instrument invented in the mid-20th century, is a harmonically complex system making fundamental pitch detection a difficult task. Initial experiments using state-of-the-art pitch detection algorithms have shown significant difficulties in detecting the pitch of individual steelpan notes. This paper presents a method of improved pitch detection accuracy by combining music information retrieval techniques with machine learning algorithms. An audio sample set consisting of thousands of steelpan notes from ten different tenor steelpans is used to quantitatively evaluate the proposed methodology. Low-level audio features are extracted from the audio samples and used to train a machine learning algorithm which identifies the salient features for pitch estimation. This method’s performance is measured against the current state-of-the-art pitch detection methods, pYIN (a probabilistic autocorrelation method) and CRéPE (a convolutional neural network-based method), showing improved performance at steelpan pitch detection. The machine learning model developed for this paper using this methodology is focused on the tenor steelpan but is intended to lead to a more generalized model for pitch detection of all types of steelpans.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".