Variable Length Windowing to Improve Non-Negative Matrix Factorization of Music Signals
Bibliographic record
Abstract
Non-negative matrix factorization(NMF) has shown positive results in learning musical notes over other blind source separation techniques like independent component analysis (ICA) and principal component analysis (PCA). The magnitude short time Fourier transform (STFT) is typically given as input for the non-negative matrix factorization algorithm for learning. Non-negative matrix factorization treats each frame of the magnitude short time Fourier transform as independent and identically distributed vectors. Due to fixed length windowing, a single note might be spread across multiple frames with varying proportions of the note structure. Improving the stationarity characteristics of the signal within the STFT is expected to improve the qualitative performance of note extraction by the NMF algorithm. To this extent, we propose a signal dependent variable length window based STFT to effectively capture the stationarity of the signal within each frame of the magnitude STFT.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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 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".