Bibliographic record
Abstract
Recently, a new tool known as Nonnegative Matrix Factorization (NMF) has presented itself as a formidable and useful tool for providing a parts based representation of matrix data.It has been applied with success in audio signal processing for topics such as blind source separation (BSS), music transcription and for representing musical and/or speech mixtures as additively occurring nonnegative representations of audio components.In the STFT domain, this is due to the fact that Fourier coefficients can be stored and processed as either matrices or tensors, and the additive mixtures of sounds can be further parametrized and factorized in some way to output a parts based time-frequency representation of the sound mixtures.In this thesis, we consider both speech mixtures and musical mixtures, as valid types of mixtures to be separated by the proposed algorithm.This thesis presents research and a proposed algorithm that addresses the problem of underdetermined multichannel frequency domain BSS, and investigates spatial covariance matrix (SCM) based NMF and single channel CNMF algorithms as applied to complex (as opposed to nonnegative) STFT coefficients.The research also investigates K-means clustering applied to interchannel frequency dependent phase differences in order to achieve source separation using SCM NMF based techniques.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".