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Symbolic Music Representations for Classification Tasks: A Systematic Evaluation
Music Information Retrieval (MIR) has seen a recent surge in deep
learning-based approaches, which often involve encoding symbolic music (i.e.,
music represented in terms of discrete note events) in an image-like or
language like fashion. However, symbolic music is neither an image nor a
sentence, and research in the symbolic domain lacks a comprehensive overview of
the different available representations. In this paper, we investigate matrix
(piano roll), sequence, and graph representations and their corresponding
neural architectures, in combination with symbolic scores and performances on
three piece-level classification tasks. We also introduce a novel graph
representation for symbolic performances and explore the capability of graph
representations in global classification tasks. Our systematic evaluation shows
advantages and limitations of each input representation. Our results suggest
that the graph representation, as the newest and least explored among the three
approaches, exhibits promising performance, while being more light-weight in
training
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μ μ°½μμ±μ μμ μ μΈ μμ± νμ§λ‘ νμ₯ν μ μλ€λ κ°λ₯μ±μ μμ¬νλ€.Chapter 1 Introduction 1
1.1 Motivation 5
1.2 Definitions 8
1.3 Tasks of Interest 10
1.3.1 Generation Quality 10
1.3.2 Controllability 12
1.4 Approaches 13
1.4.1 Modeling Musical Hierarchy 14
1.4.2 Regularizing Latent Representations 16
1.4.3 Target Tasks 18
1.5 Outline of the Thesis 19
Chapter 2 Background 22
2.1 Music Generation Tasks 23
2.1.1 Melody Harmonization 23
2.1.2 Expressive Performance Rendering 25
2.2 Structure-enhanced Music Generation 27
2.2.1 Hierarchical Music Generation 27
2.2.2 Transformer-based Music Generation 28
2.3 Disentanglement Learning 29
2.3.1 Unsupervised Approaches 30
2.3.2 Supervised Approaches 30
2.3.3 Self-supervised Approaches 31
2.4 Controllable Music Generation 32
2.4.1 Score Generation 32
2.4.2 Performance Rendering 33
2.5 Summary 34
Chapter 3 Translating Melody to Chord: Structured and Flexible Harmonization of Melody with Transformer 36
3.1 Introduction 36
3.2 Proposed Methods 41
3.2.1 Standard Transformer Model (STHarm) 41
3.2.2 Variational Transformer Model (VTHarm) 44
3.2.3 Regularized Variational Transformer Model (rVTHarm) 46
3.2.4 Training Objectives 47
3.3 Experimental Settings 48
3.3.1 Datasets 49
3.3.2 Comparative Methods 50
3.3.3 Training 50
3.3.4 Metrics 51
3.4 Evaluation 56
3.4.1 Chord Coherence and Diversity 57
3.4.2 Harmonic Similarity to Human 59
3.4.3 Controlling Chord Complexity 60
3.4.4 Subjective Evaluation 62
3.4.5 Qualitative Results 67
3.4.6 Ablation Study 73
3.5 Conclusion and Future Work 74
Chapter 4 Sketching the Expression: Flexible Rendering of Expressive Piano Performance with Self-supervised Learning 76
4.1 Introduction 76
4.2 Proposed Methods 79
4.2.1 Data Representation 79
4.2.2 Modeling Musical Hierarchy 80
4.2.3 Overall Network Architecture 81
4.2.4 Regularizing the Latent Variables 84
4.2.5 Overall Objective 86
4.3 Experimental Settings 87
4.3.1 Dataset and Implementation 87
4.3.2 Comparative Methods 88
4.4 Evaluation 88
4.4.1 Generation Quality 89
4.4.2 Disentangling Latent Representations 90
4.4.3 Controllability of Expressive Attributes 91
4.4.4 KL Divergence 93
4.4.5 Ablation Study 94
4.4.6 Subjective Evaluation 95
4.4.7 Qualitative Examples 97
4.4.8 Extent of Control 100
4.5 Conclusion 102
Chapter 5 Conclusion and Future Work 103
5.1 Conclusion 103
5.2 Future Work 106
5.2.1 Deeper Investigation of Controllable Factors 106
5.2.2 More Analysis of Qualitative Evaluation Results 107
5.2.3 Improving Diversity and Scale of Dataset 108
Bibliography 109
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