Monaural Music Separation via Supervised Non-Negative Matrix Factor with Side-Information
Bibliographic record
Abstract
In this dissertation, a supervised source template nonnegative matrix factorization (NMF) algorithm is proposed to solve the monaural music source separation problem.Different from the previous state-of-the-art algorithms, the basic theoretical concept of the proposed algorithm considers the spectrogram from an audio mixture as linear combinations of note templates.Having prior knowledge of these note templates for each source, we can estimate and determine the activities of each template in recordings to build a mask of each source.Through the masks, the audio of target tracks can be reconstructed.We reviewed previous research on source separation for monaural music audio separation and compared these work with our proposed algorithm not only in mathematical expressions but also in separation performances.First, the prior knowledge of note templates is informed by musical instrument audio dataset.The spectrograms from these instruments are obtained and factored into a source resonance character matrix and a source impulse excitation matrix by assuming that the spectrum of the different notes are formed by the resonance effects from an impulse excitation.Secondary, according to the prior informed note templates, their onset-offset-like features are estimated by using the multiplicative update rule and supervised by the proposed pitch-checking algorithm to remove misleading estimations.Finally, the supervised note onset-offset-like features alternatively become a constraint to help the proposed model evolve its prior informed note templates into the forms given by the recorded instruments.We employed the TRIOS and the Bach-10 dataset for our multi-source separation performance tests.Among the source separation algorithms, our proposed supervised source template NMF and the state-of-the-art algorithms including the sound-prism and the Oracle-toolbox methods were selected to make comparisons.Furthermore, we added white Gaussian noise into the audio mixture to simulate the background full of the random noise to test the noise characteristics of each algorithm.The iii First of all, I wish to thank Prof. Richard Dansereau, my supervisor, for bringing me into the fantastic world of Artificial Intelligence and Machine Learning, continually giving me generous funding support, confidence and knowledge, and guiding me on this road.Special thanks to Tony Wacheski and Sean Kormilo who are the CEO, Co-Founder and Senior Software Engineer at Anystone Inc., who have opened a gate for me by programming and coding our research findings into commercial software.Finally, my sincere appreciation to my wife, especially for
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.002 |
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".