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

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3.4 KI-CBR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.4.1 NOOS: a Knowledge Representation Language . . . . . 44 3.5 Application of KI-CBR for Expressive Music Performance . . 45 3.5.1 Music Analysis . . . . . . . . . . . . . . . . . . . . . . 47 3.5.2 Melodic Similarity . . . . . . . . . . . . . . . . . . . . 50 4 Research Goals 57 4.1 The Broad Perspective . . . . . . . . . . . . . . . . . . . . . . 57 4.2 Focus of Attention in This Research . . . . . . . . . . . . . . . 59 5 Proposed Approach and Architecture 63 5.1 General Overview of the Tempo-Express System . . . . . . . . 63 5.2 Analysis and Synthesis of Audio . . . . . . . . . . . . . . . . . 64 5.3 Construction of Cases . . . . . . . . . . . . . . . . . . . . . . . 64 5.3.1 Performance Annotation . . . . . . . . . . . . . . . . . 65 5.3.2 Musical Analysis: An I/R Parser for Monophonic Melodies 74 5.4 The Case Base . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.5 Problem Solving . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.5.1 Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.5.2 Reuse . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.5.3 Retain . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 6 Conclusions and Planned Research 89 6.1 Resume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6.2 Current Results and Contributions . . . . . . . . . . . . . . . 90 6.2.1 Comparison of Melodic Distance Measures . . . . . . . 90 6.2.2 Performance Annotation . . . . . . . . . . . . . . . . . 91 6.2.3 Evolutionary Optimization of the Performance Annotation Process . . . . . . . . . . . . . . . . . . . . . . . 91 6.2.4 Development of a Prototype of the System Architecture 92 6.3 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . 93 6.3.1 Better Performance Representation . . . . . . . . . . . 93 6.3.2 Tools for Evaluating the Quality of the Case Base . . . 94 6.3.3 System Evaluation . . . . . . . . . . . . . . . . . . . . 95 4
List of Figures 2.1 Time Maps; (a) Two TM’s f and g composed (s ′′ = f(s ′ ) = f(g(s))); (b) Two TM’s f and g concatenated by aligning score time. . 20 3.1 Problem solving in CBR. . . . . . . . . . . . . . . . . . . . . . 31 3.2 Lazy and eager learning and their roles in the use and construction of memory. . . . . . . . . . . . . . . . . . . . . . . . 33 3.3 The Case Based Reasoning cycle, which shows the relations among the four different stages in problem solving: Retrieve, Reuse, Revise, Retain. . . . . . . . . . . . . . . . . . . . . . . 35 3.4 (a) The relation between score, performance requirements, and performances. (b) Two types of case layout, both representing the constellation shown in (a). . . . . . . . . . . . . . . . . . . 46 3.5 Eight of the basic structures of the I/R model. . . . . . . . . . 48 3.6 First measures of ‘All of me’, with corresponding I/R structures. 50 4.1 Some basic music performance concepts and their relations. . . 58 4.2 Diagram of the system architecture . . . . . . . . . . . . . . . 60 5.1 General view of Tempo-Express modules. . . . . . . . . . . . . 64 5.2 A hierarchical representation of edit operations for performance annotation. The unboxed names denote abstract classes; the light gray boxes denote concrete classes, and the dark gray boxes denote ‘hyper concrete’ classes. . . . . . . . . . . . . . . 67 5.3 Cost values for insertion and ornamentation operations as a function of note duration. . . . . . . . . . . . . . . . . . . . . 71 5.4 Estimated parameter values for two different training sets (Tr1 and Tr2). Three runs were done for each set (a, b, and c). The x-axis shows the nine different parameters of the cost functions (see section 5.3.1). For each parameter the values are shown for each run on both training sets . . . . . . . . . . . . . . . . 73 5
Page 1 and 2: Global Musical Tempo Transformation
Page 3: Contents 1 Introduction 9 1.1 Expre
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
Page 55 and 56:
Computing Similarity by compression
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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
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Performance recording .wav Performa
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is more appropriate for comparing w
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events corresponding to Transformat
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insertion, ornamentation, transform
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settings for larger sets of perform
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Tr1a Tr1b Tr1c Tr2a Tr2b Tr2c Error
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Tagging Interval Pairs This step co
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of the musical score (notes and cho
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I/R based retrieval In the second s
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In constructive adaptation, partial
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ing rules that specify which annota
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een scarcely used, or not at all),
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Chapter 6 Conclusions and Planned R
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6.2.2 Performance Annotation We hav
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Related publications: • M. Gracht
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The other direction of evaluating t
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Bibliography [1] A. Aamodt. Towards
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[20] Sergio Canazza, Giovanni De Po
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[42] A. Gabrielsson. Once again: Th
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[65] V. Iyer, J. Bilmes, M. Wright,
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[93] E. Plaza and J. Ll. Arcos. Con
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[117] N.P. Todd. A computational mo
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melody lead, 12 Model Based Reasoni
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