Global Musical Tempo Transformations using Case Based ... - OFAI

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Acknowledgments I would like to express my gratitude to Ramon López de Mántaras and Josep Lluís Arcos. In the first place for offering me a great place to work, to study, and to meet people at the Artificial Intelligence Research Institute (IIIA) in Barcelona. Secondly, for sharing their experience through invaluable advice, throughout the time I have worked with them. And I also want to mention Lieveke van Heck. We have been together for a lot of years now and she showed patience, soothed doubts and raised spirits. Thanks! 2
Contents 1 Introduction 9 1.1 Expressiveness in Musical Performances . . . . . . . . . . . . . 9 1.1.1 Functions of Performance . . . . . . . . . . . . . . . . 10 1.1.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . 13 1.2 Musical Tempo Transformations . . . . . . . . . . . . . . . . . 14 2 Music Performance Research 17 2.1 Fundamental and Methodological Issues in Music Performance Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.1.1 Definitions of Expression . . . . . . . . . . . . . . . . . 18 2.1.2 Representation of Timing and Tempo . . . . . . . . . . 19 2.1.3 Perception of Time and Rhythm . . . . . . . . . . . . . 21 2.2 Expressive Music Generation . . . . . . . . . . . . . . . . . . . 21 2.2.1 Machine Learning, Musical Data-mining and Performance Prediction . . . . . . . . . . . . . . . . . . . . . 22 2.2.2 A Grammar for Musical Expression . . . . . . . . . . . 24 2.2.3 A Partial Model of Performance: Predicting Rubato . . 26 2.2.4 SaxEx: A Case Based Reasoning System for Expressive Music Generation . . . . . . . . . . . . . . . . . . . . . 27 3 Case Based Reasoning 29 3.1 What is CBR? . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Why CBR? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.2.1 Lazy versus Eager Learners . . . . . . . . . . . . . . . 32 3.3 The CBR-cycle . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.1 Case Representation . . . . . . . . . . . . . . . . . . . 34 3.3.2 The Retrieve Task . . . . . . . . . . . . . . . . . . . . 36 3.3.3 The Reuse Task . . . . . . . . . . . . . . . . . . . . . . 38 3.3.4 The Revise Task . . . . . . . . . . . . . . . . . . . . . 40 3.3.5 The Retain Task . . . . . . . . . . . . . . . . . . . . . 41 3.3.6 Case Base Maintenance . . . . . . . . . . . . . . . . . . 41 3
Page 1: Global Musical Tempo Transformation
Page 5 and 6: List of Figures 2.1 Time Maps; (a)
Page 7: List of Tables 5.1 Annotation error
Page 10 and 11: nominal performance). Even when (sk
Page 12 and 13: sity of ways in which a piece can b
Page 14 and 15: Another fundamental question is whe
Page 16 and 17: gain importance, which could accoun
Page 18 and 19: present throughout the whole piece.
Page 20 and 21: t’ g t t’ t’ t g f f s’ t
Page 22 and 23: music expressively, they provide a
Page 24 and 25: dependencies between curves at diff
Page 26 and 27: Harmonic intonation adjust pitches
Page 29 and 30: Chapter 3 Case Based Reasoning In t
Page 31 and 32: Solved Problems Unsolved Problem Pr
Page 33 and 34: Eager Learning Domain Model Problem
Page 35 and 36: Problem New Case RETRIEVE Learned C
Page 37 and 38: lem descriptions of the cases in th
Page 39 and 40: solutions (see [22, 3, 123]). We wi
Page 41 and 42: 3.3.5 The Retain Task When a good s
Page 43 and 44: order to treat case information in
Page 45 and 46: which use the knowledge of the doma
Page 47 and 48: ased on similarity between problem
Page 49 and 50: According to the I/R model, the abo
Page 51 and 52: Retrieve task of the system. The re
Page 53 and 54:
{〈x 1 , w 1 〉, · · · 〈x m
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Computing Similarity by compression
Page 57 and 58:
Chapter 4 Research Goals 4.1 The Br
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4.2 Focus of Attention in This Rese
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Case Retrieval Defining a way to de
Page 64 and 65:
Performance recording .wav Performa
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is more appropriate for comparing w
Page 68 and 69:
events corresponding to Transformat
Page 70 and 71:
insertion, ornamentation, transform
Page 72 and 73:
settings for larger sets of perform
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Tr1a Tr1b Tr1c Tr2a Tr2b Tr2c Error
Page 76 and 77:
Tagging Interval Pairs This step co
Page 78 and 79:
of the musical score (notes and cho
Page 80 and 81:
I/R based retrieval In the second s
Page 82 and 83:
In constructive adaptation, partial
Page 84 and 85:
ing rules that specify which annota
Page 86 and 87:
een scarcely used, or not at all),
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Chapter 6 Conclusions and Planned R
Page 91 and 92:
6.2.2 Performance Annotation We hav
Page 93 and 94:
Related publications: • M. Gracht
Page 95 and 96:
The other direction of evaluating t
Page 97 and 98:
Bibliography [1] A. Aamodt. Towards
Page 99 and 100:
[20] Sergio Canazza, Giovanni De Po
Page 101 and 102:
[42] A. Gabrielsson. Once again: Th
Page 103 and 104:
[65] V. Iyer, J. Bilmes, M. Wright,
Page 105 and 106:
[93] E. Plaza and J. Ll. Arcos. Con
Page 107 and 108:
[117] N.P. Todd. A computational mo
Page 109:
melody lead, 12 Model Based Reasoni
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