Combining prior-knowledge and grouping cues using a spectral clustering approach.
Bibliographic record
Abstract
Learning happens at the boundary interactions beween prior knowledge and incoming data. The same interplay takes place when trying to analyze and separate complex mixtures of sound sources such as music. Many approaches to this problem can be broadly categorized as either model based or grouping based. Although it is known that our perceptual system utilizes both of these types of processing, building such systems computationally has been challenging. As a result most existing systems either rely on prior source models or are solely based on grouping cues. In this work it is argued that formulating this integration problem as clustering based on similarities between time-frequency atoms provides an expressive but discipined approach to building sound source characterization and separation systems and to evaluating their performance. After describing the main components of such an architecture, we describe a concrete realization that is based on spectral clustering of a sinusoidal representation. We show how this approach can be used to model both traditional grouping cues such as frequency and amplitude continuity as well as other types of information and prior knowledge such as stereo panning, onsets, harmonicity, and timbre models for specific integration will also be described.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".