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Table of contents
Acknowledgments
Abstract
Résumé en français
1 Introduction
1.1 Approach
1.2 Organization
1.3 Contributions
I From Modeling Anticipation to Anticipatory Modeling
2 Modeling Musical Anticipation
2.1 Psychology of musical expectation
2.1.1 Experimental Research Scopes
2.1.2 Auditory Learning
2.1.3 Concurrent and Competitive Representations
2.1.4 Mental Representations of Expectation
2.2 Anticipation Defined
2.2.1 Anticipation in view of Expectation
2.2.2 Anticipation in view of Enaction
2.2.3 Anticipation in view of Computation
2.3 Models of Musical Expectation
2.3.1 Music Theoretic Models
2.3.2 Automatic Learning Models
2.3.3 Information Theoretic Models
2.4 Modeling Investigations
2.4.1 Imperfect Heuristics and Naive Realism
2.4.2 Over-intellectualization of the intellect
2.4.3 Scientific pluralism
2.5 Summary
3 Anticipatory Modeling 27
3.1 Anticipatory Computing
3.2 General Modeling Framework
3.2.1 Markov Decision Process Framework
3.2.2 Interactive Learning in an Environment
3.3 Distinctions of Anticipatory Behavior
3.3.1 Implicit Anticipation
3.3.2 Payoff Anticipation
3.3.3 Sensorial Anticipation
3.3.4 State Anticipation
3.4 Learning Approaches
3.4.1 Reinforcement Learning
3.4.2 Learning Classifier Systems
3.5 Modeling Implications
3.5.1 Information as Available
3.5.2 Interactive and on-line Learning
3.5.3 Multimodal Interaction and Modeling
II What to Expect
4 Music Information Geometry
4.1 General Discussions
4.2 Preliminaries
4.2.1 Information Geometry of Statistical Structures
4.2.2 Elements of Bregman Geometry
4.2.3 Exponential Family of Distributions
4.2.4 Bregman Geometry and Exponential distributions
4.3 Music Information Geometry
4.3.1 Methodology
4.3.2 Data IR
4.3.3 Model IR
4.4 From Divergence to Similarity Metric
4.4.1 Symmetrized Bregman Divergences
4.4.2 Triangle Inequality
4.5 Incremental Model Formations
4.6 Discussions
5 Methods of Information Access
5.1 Incremental Clustering and Structure Discovery
5.1.1 Related Works
5.1.2 Audio Oracle Data Structure
5.1.3 Audio Oracle Learning and Construction
5.1.4 Sample Results
5.1.5 Discussions
5.2 Guidage: Fast Query-Based Information Retrieval
5.2.1 Research Scope
5.2.2 Related Works
5.2.3 General Framework
5.2.4 Search Domain and Meta Data
5.2.5 Guidage Algorithm
5.2.6 Resynthesis
5.2.7 Sample Applications and Results
5.2.8 Discussions
III How to Expect
6 Adaptive and Interactive Learning
6.1 Introduction
6.2 Background on Stochastic Music Modeling
6.2.1 Memory Models
6.2.2 Approaches to Statistical Learning
6.2.3 Approaches to Planning and Interaction
6.3 General Discussions
6.4 Active Learning Architecture
6.4.1 Audio Oracles for Memory Models
6.4.2 Guidage for Active Selection
6.5 Anticipatory Learning
6.5.1 Competitive and Collaborative learning
6.5.2 Memory-based Learning
6.6 Active Learning Algorithm
6.6.1 Model Complexity
6.7 Results and Experiments
6.7.1 Knowledge-Based Interactions
6.7.2 Anticipatory Style Imitation and Automatic Improvisation
6.8 Discussions
IV When to Expect
7 Anticipatory Synchronization
7.1 Introduction
7.2 Background
7.2.1 Score Following Research
7.2.2 Cognitive Foundations of Musical Time
7.2.3 Compositional Foundations of Time
7.2.4 Probabilistic Models of Time
7.3 General Framework
7.3.1 Anticipatory Multimodal Inference
7.3.2 Hybrid Models of Time
7.4 Inference Formulation
7.5 Stochastic model of time in music performance
7.5.1 Attentional Model of Tempo
7.5.2 Tempo Agent and Decoding
7.5.3 Survival Distribution Model
7.6 Music Score Model
7.6.1 Basic Events
7.6.2 Special timed events
7.7 Observation Model
7.8 Evaluation
7.8.1 Evaluation of Tempo Prediction
7.8.2 Evaluation over synthesized audio from score
7.8.3 Evaluation of real-time Alignment
7.9 Discussions
8 Towards Writing of Time and Interaction in Computer Music
8.1 Background
8.1.1 Computer Music Language Paradigms
8.1.2 Practical Status
8.1.3 Compositional Status
8.1.4 Research Status
8.2 Antescofo: A preliminary tool for writing of time and interaction
8.2.1 Motivations
8.2.2 General Architecture
8.3 Antescofo: A modular and concurrent synchronizer
8.4 Antescofo’s Score Semantics
8.4.1 Event Declarations
8.4.2 Control Commands
8.4.3 Action Declarations
8.5 From the Time of Composition to the Time of Performance in Antescofo
8.6 Discussions and Future Directions
8.6.1 Augmenting the Semantics of Interaction
8.6.2 Multimodal Coordination
8.6.3 Intuitive Interfaces
8.6.4 Relating to the Community
9 Conclusions
9.1 The story so far
9.2 Outlook
V Appendices
A Supplemental Material for part II
A.1 Properties of Multinomial Manifolds
A.2 Bregman Divergence Symmetrization
A.2.1 Geodesic-walk Algorithm for Multinomial Manifolds
B Supplemental Material for Part IV
B.1 Derivation of Forward Recursion
B.2 Raphael’s Tempo Inference Model
References
