Using convolutional neural networks to estimate pitch directly from steelpan audio signals
Bibliographic record
Abstract
Estimating the pitch, or fundamental frequency, of a monophonic audio signal is a fundamental task in computational audio analysis with many downstream applications such as automatic transcription. The current general state of the art method for pitch detection is CRéPE: a Convolutional Representation for Pitch Estimation. CRéPE is a deep convolutional neural network designed to estimate the fundamental frequency of an audio signal directly from the waveform. However, CRéPE, and other general pitch detection methods, do not perform well on steelpan audio. This is likely due to the steelpan's complex spectral characteristics that differentiate it timbrally from other sound sources. We combine a deep convolutional neural network architecture based on CRéPE with a training dataset of steelpan audio from several distinct sounding tenor steelpans to achieve improved tenor steelpan pitch detection directly from the audio signal. We assess our model's ability to generalize by evaluating it with a test dataset that includes audio samples from steelpans that have no samples as part of the training set and compare these results to CRéPE's performance on the same test dataset.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".