Dimensions of Timbre Models

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Table of contents

1 Introduction 
I Epistemology 
2 Dimensions of Timbre 
2.1 Everything but pitch and loudness
2.2 Psychophysical studies
2.3 Automatic recognition of monophonic timbres
2.4 Towards polyphonic textures
2.4.1 The demand of Electronic Music Distribution
2.4.2 The lack of perceptive models
2.5 Implicit modelling
2.6 Thesis Overview: Ten Experiments
3 Dimensions of Timbre Models 
3.1 The Prototypical algorithm
3.1.1 Feature Extraction
3.1.2 Feature Distribution Modelling
3.1.3 Distance Measure
3.2 MIR Design Patterns and Heuristics
3.2.1 Pattern: Tuning feature parameters
3.2.2 Pattern: Tuning model parameters
3.2.3 Pattern: Feature Equivalence
3.2.4 Pattern: Model Equivalence
3.2.5 Pattern: Feature Composition
3.2.6 Pattern: Cross-Fertilisation
3.2.7 Pattern: Modelling Dynamics
3.2.8 Pattern: Higher-level knowledge
3.3 Conclusion
II Experiments 
4 Experiment 1: The Glass Ceiling 
4.1 Experiment
4.2 Method
4.2.1 Explicit modelling
4.2.2 Ground Truth
4.2.3 Evaluation Metric
4.3 Tools
4.3.1 Architecture
4.3.2 Implementations
4.3.3 Algorithms
4.4 Results
4.4.1 Best Results
4.4.2 Significance
4.4.3 Dynamics don’t improve
4.4.4 “Everything performs the same”
4.4.5 Existence of a glass ceiling
4.4.6 False Positives are very bad matches
4.4.7 Existence of hubs
5 Experiment 2: The Usefulness of Dynamics 
5.1 The paradox of Dynamics
5.2 Hypothesis
5.3 Method
5.3.1 Databases
5.3.2 Algorithms
5.3.3 Evaluation Procedure
5.4 Results
6 Experiments 3-8: Understanding Hubs
6.1 Definition
6.2 Why this may be an important problem
6.3 Measures of hubness
6.3.1 Number of occurrences
6.3.2 Neighbor angle
6.3.3 Correlation between measures
6.4 Power-law Distribution
6.5 Experiment 3: Features or Model ?
6.5.1 Hypothesis
6.5.2 Experiment
6.5.3 Results
6.6 Experiment 4: Influence of modelling
6.6.1 Hypothesis
6.6.2 Experiment
6.6.3 Results
6.7 Experiment 5: Intrinsic or extrinsic to songs ?
6.7.1 Hypothesis
6.7.2 Experiment
6.7.3 Results
6.8 Experiment 6: The seductive, but probably wrong, hypothesis of equivalence classes
6.8.1 Hypothesis
6.8.2 Experiment
6.8.3 Results
6.9 Experiment 7: On homogeneity
6.9.1 Hypothesis
6.9.2 Experiment
6.9.3 Results
6.10 Experiment 8: Are hubs a structural property of the algorithms ?
6.10.1 Hypothesis
6.10.2 Experiment
6.10.3 Results
7 Experiments 9 & 10: Grounding 
7.1 Experiment 9: Inferring high-level descriptions with timbre similarity
7.1.1 Material
7.1.2 Methods
7.1.3 Results
7.2 Experiment 10: The use of context
7.2.1 Method
7.2.2 Results
7.2.3 Exploiting correlations with decision trees
7.3 An operational model for grounding high-level descriptions
7.3.1 Algorithm
7.3.2 Preliminary results
8 Conclusion: Toward Cognitive Models 
III Synthese en francais – Digest in French 
IV Appendices 
A Composition of the test database 
B Experiment 1 – Details 
B.1 Tuning feature and model parameters (patterns 3.2.1 and 3.2.2)
B.1.1 influence of SR
B.1.2 influence of DSR
B.1.3 influence of N,M
B.1.4 influence of Windows Size
B.2 Alternative distance measure (pattern 3.2.4)
B.3 Feature Composition (pattern 3.2.5)
B.3.1 Processing commonly used in Speech Recognition
B.3.2 Spectral Contrast
B.4 Feature Equivalence (pattern 3.2.3)
B.5 Modelling dynamics (pattern 3.2.7)
B.5.1 Delta and Acceleration Coefficients
B.5.2 Texture windows
B.5.3 Dynamic modeling with hidden Markov models
B.6 Building in knowledge about note structure (pattern 3.2.8)
B.6.1 Removing noisy frames
B.6.2 Note Segmentation
B.6.3 Comparison of the 2 approaches
B.7 Model Equivalence (pattern 3.2.4)
B.7.1 Pampalk’s Spectrum histograms
B.7.2 MFCCs Histograms
B.8 Borrowing from Image Texture Analysis (pattern 3.2.6)
B.8.1 Image Texture Features
B.8.2 Application to Audio
B.8.3 Vector Quantization
B.8.4 Conclusions of texture analysis
C Comparison of implementation performance 
C.1 Feature Extraction
C.2 Distribution Modelling
D Nearest Neighbor Algorithm 
D.1 Tradeoff between Precision and CPU-time
D.2 Algorithm formulation
D.2.1 Definitions and Assumptions
D.2.2 Efficiency
D.2.3 Implementation
D.3 Application to Timbre Similarity
D.3.1 The Precision-Cputime Tradeoff
D.3.2 Formulation of the Problem
D.3.3 Practical Implementation
D.3.4 Results
E Multiscale segmentation 
F Measures of Hubs 
F.1 Rank-based metrics
F.2 Distance-based metrics
F.3 Correlation between measures
F.3.1 Number of N-occurrences
F.3.2 Number of N-occurrences and Neighbor difference and angle
F.3.3 Number of N-occurrences and TI violations
G Pearson’s Â2-test of independence