Musical pitch tracking using internal model control based frequency cancellation
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
A new method for pitch estimation of musical sound signal is presented in this paper. This method is based on the behavior of a notch filter in an error feedback system, and was first developed for identification of periodic signals with uncertain frequency. Unlike many previous methods, which are based on frequency representation in time window of the music signal, our algorithm is based on instantaneous 'measurements' of the frequency in real-time. Because of the high accuracy of the frequencies and the magnitudes we obtained, this method may also be used to verify the types of the instruments and even the vibratos. Simulation results show that the presented approach operates reliably in monophonic case with a highly accurate frequency estimation. The same method has been extended to the polyphonic setting. At present, we are restricting input with a relatively strong fundamental frequency. This paper is a progress report, we hope to extend our work greatly in the future.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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