Modeling Grouping Cues for Auditory Scene Analysis Using a Spectral Clustering Formulation
Bibliographic record
Abstract
Computational Auditory Scene Analysis (CASA) is challenging problem for which many different approaches have been proposed. These approaches can be based on statistical and signal processing methods such as Independent Component Analysis or can be based on our current knowledge about human auditory perception. Learning happens at the boundary interactions between prior knowledge and incoming data. Separating complex mixtures of sound sources such as music requires a complex interplay between prior knowledge and analysis of incoming data. Many approaches to CASA can also 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 chapter the authors argue that formulating this integration problem as clustering based on similarities between time-frequency atoms provides an expressive yet disciplined approach to building sound source characterization and separation systems and evaluating their performance. After describing the main components of such an architecture, the authors describe a concrete realization that is based on spectral clustering of a sinusoidal representation. They 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 onsets, harmonicity and timbre-models for specific instruments. Experiments supporting their approach to integration are also described. The description also covers issues of software architecture, implementation and efficiency, which are frequently not analyzed in depth for many existing algorithms. The resulting system exhibits practical performance (approximately real-time) with consistent results without requiring example-specific parameter optimization and is available as part of the Marsyas open source audio processing framework.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".