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Modeling Musical Anticipation: from the Time of Music to the Music of Time

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Modeling Musical Anticipation: from the Time of Music to the Music of Time UNIVERSITY OF CALIFORNIA, SAN DIEGO Modeling Musical Anticipation: From the time of music to the music of time A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Music by Arshia Cont Committee in charge: Shlomo Dubnov, Chair Alain de Cheveigné Philippe Manoury Miller Puckette Lawrence Saul David Wessel 2008 Copyright Arshia Cont, 2008 All rights reserved. The dissertation of Arshia Cont is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Chair University of California, San Diego 2008 iii DEDICATION !"#$%&'!()*+,-.&'!() /0123456!78. 9: CD JKL $ 3;<=>?@6AB 5EFGHI MN Dedicated to ones who dedicate... my parents Mansoureh Daneshkazemi and Gholamhossen Cont iv ! EPIGRAPH “I think that the search for a universal answer to the questions raised by musical experi- ence will never be completely fulfilled; but we know that a question raised is often more significant than the answer received. Only a reckless spirit, today, would try to give a total explanation of music, but anyone who would never pose the problem is even more reckless.” Remembering the future LUCIANO BERIO v TABLE OF CONTENTS Signature Page............................................ iii Dedication.............................................. iv Epigraph...............................................v Table of Contents.......................................... vi List of Figures............................................ xi List of Tables............................................ xiii Acknowledgements........................................ xiv Vita................................................... xvii Abstract................................................ xviii Chapter 1. Introduction......................................1 1.1. Approach........................................3 1.2. Organization......................................5 1.3. Contributions......................................7 I From Modeling Anticipation to Anticipatory Modeling9 Chapter 2. Modeling Musical Anticipation......................... 10 2.1. Psychology of musical expectation........................ 11 2.1.1. Experimental Research Scopes..................... 11 2.1.2. Auditory Learning.............................. 13 2.1.3. Concurrent and Competitive Representations............ 14 2.1.4. Mental Representations of Expectation................ 15 2.2. Anticipation Defined................................. 17 2.2.1. Anticipation in view of Expectation.................. 17 2.2.2. Anticipation in view of Enaction.................... 18 2.2.3. Anticipation in view of Computation.................. 19 2.3. Models of Musical Expectation.......................... 20 2.3.1. Music Theoretic Models.......................... 21 2.3.2. Automatic Learning Models....................... 23 2.3.3. Information Theoretic Models...................... 25 2.4. Modeling Investigations............................... 29 2.4.1. Imperfect Heuristics and Naive Realism............... 30 vi 2.4.2. Over-intellectualization of the intellect................ 33 2.4.3. Scientific pluralism............................. 34 2.5. Summary........................................ 35 Chapter 3. Anticipatory Modeling............................... 38 3.1. Anticipatory Computing............................... 39 3.2. General Modeling Framework........................... 41 3.2.1. Markov Decision Process Framework................. 42 3.2.2. Interactive Learning in an Environment................ 44 3.3. Distinctions of Anticipatory Behavior...................... 45 3.3.1. Implicit Anticipation............................ 46 3.3.2. Payoff Anticipation............................. 47 3.3.3. Sensorial Anticipation........................... 47 3.3.4. State Anticipation.............................. 48 3.4. Learning Approaches................................. 49 3.4.1. Reinforcement Learning.......................... 50 3.4.2. Learning Classifier Systems....................... 51 3.5. Modeling Implications................................ 52 3.5.1. Information as Available.......................... 52 3.5.2. Interactive and on-line Learning..................... 53 3.5.3. Multimodal Interaction and Modeling................. 54 II What to Expect 56 Chapter 4. Music Information Geometry........................... 57 4.1. General Discussions................................. 57 4.2. Preliminaries...................................... 60 4.2.1. Information Geometry of Statistical Structures........... 61 4.2.2. Elements of Bregman Geometry..................... 63 4.2.3. Exponential Family of Distributions.................. 68 4.2.4. Bregman Geometry and Exponential distributions......... 70 4.3. Music Information Geometry........................... 74 4.3.1. Methodology................................. 74 4.3.2. Data IR..................................... 76 4.3.3. Model IR.................................... 77 4.4. From Divergence to Similarity Metric...................... 79 4.4.1. Symmetrized Bregman Divergences.................. 81 4.4.2. Triangle Inequality............................. 82 4.5. Incremental Model Formations.......................... 83 4.6. Discussions....................................... 87 vii Chapter 5. Methods of Information Access......................... 89 5.1. Incremental Clustering and Structure Discovery............... 89 5.1.1. Related Works................................ 90 5.1.2. Audio Oracle Data Structure....................... 93 5.1.3. Audio Oracle Learning and Construction............... 96 5.1.4. Sample Results................................ 99 5.1.5. Discussions.................................. 102 5.2. Guidage: Fast Query-Based Information Retrieval.............. 104 5.2.1. Research Scope............................... 105 5.2.2. Related Works................................ 107 5.2.3. General Framework............................. 109 5.2.4. Search Domain and Meta Data...................... 110 5.2.5. Guidage Algorithm............................. 112 5.2.6. Resynthesis.................................. 116 5.2.7. Sample Applications and Results.................... 117 5.2.8. Discussions.................................. 127 III How to Expect 129 Chapter 6. Adaptive and Interactive Learning........................ 130 6.1. Introduction....................................... 131 6.2. Background on Stochastic Music Modeling.................. 133 6.2.1. Memory Models............................... 134 6.2.2. Approaches to Statistical Learning................... 139 6.2.3. Approaches to Planning and Interaction................ 140 6.3. General Discussions................................. 143 6.4. Active Learning Architecture............................ 145 6.4.1. Audio Oracles for Memory Models................... 149 6.4.2. Guidage for Active Selection....................... 152 6.5. Anticipatory Learning................................ 154 6.5.1. Competitive and Collaborative learning................ 155 6.5.2. Memory-based Learning.......................... 157 6.6. Active Learning Algorithm............................. 158 6.6.1. Model Complexity............................. 160 6.7. Results and Experiments.............................. 161 6.7.1. Knowledge-Based Interactions...................... 162 6.7.2. Anticipatory Style Imitation and Automatic Improvisation... 168 6.8. Discussions....................................... 174 IV When to Expect 176 viii Chapter 7. Anticipatory Synchronization........................... 177 7.1. Introduction....................................... 178 7.2. Background....................................... 180 7.2.1. Score Following Research......................... 180 7.2.2. Cognitive Foundations of Musical Time................ 182 7.2.3. Compositional Foundations of Time.................. 183 7.2.4. Probabilistic Models of Time....................... 185 7.3. General Framework.................................. 188 7.3.1. Anticipatory Multimodal Inference................... 189 7.3.2. Hybrid Models of Time.......................... 190 7.4. Inference Formulation................................ 192 7.5. Stochastic model of time in music performance................ 194 7.5.1. Attentional Model of Tempo....................... 194 7.5.2. Tempo Agent and Decoding....................... 198 7.5.3. Survival Distribution Model....................... 200 7.6. Music Score Model.................................. 201 7.6.1. Basic Events................................. 201 7.6.2. Special timed events............................ 202 7.7. Observation Model.................................. 205 7.8. Evaluation........................................ 207 7.8.1. Evaluation of Tempo Prediction..................... 208 7.8.2. Evaluation over synthesized audio from score............ 209 7.8.3. Evaluation of real-time Alignment................... 217 7.9. Discussions....................................... 220 Chapter 8. Towards Writing of Time and Interaction in Computer Music...... 221 8.1. Background....................................... 223 8.1.1. Computer Music Language Paradigms................. 223 8.1.2. Practical Status................................ 227 8.1.3. Compositional Status............................ 230 8.1.4. Research Status............................... 235 8.2. Antescofo: A preliminary tool for writing of time and interaction.... 237 8.2.1. Motivations.................................. 238 8.2.2. General Architecture............................ 239 8.3.
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