Factors in factorization: Does better audio source separation imply better polyphonic music transcription?
Bibliographic record
Abstract
Spectrogram factorization methods such as Non-Negative Matrix Factorization (NMF) are frequently used as a way to separate individual sound sources from complex sound mixtures. More recently, they have also been used as a first stage for the automatic transcription of polyphonic music. The problem of sound source separation is different (but related) to the problem of automatic music transcription. The output of the first is the separated audio signals corresponding to each sound source, whereas the output of the second is a symbolic representation/music score that encodes the discrete pitches/notes that are played and when they are played. Many variations of factorization methods have been proposed. Two important design choices are the way spectra are represented and what distance measures are used to compare them in the optimization used for factorization. A common assumption has been that a variant that yields better signal separation will result in better automatic transcription. In this work, we investigate experimentally this question and show that this relationship is not necessarily true.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".