Musical Instrument Digital Interface (MIDI)

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Table of contents

1 introduction 
1.1 Motivations
1.2 Dissertation organization and main contributions
2 overview 
2.1 Introduction
2.2 Symbolic music representation
2.2.1 Music as symbol
2.2.2 Symbolic music representations for computer science
2.2.3 Musical spaces
2.3 Machine learning tools
2.3.1 Basics
2.3.2 Specific tools
2.4 Embedding spaces
2.4.1 Apparition and formalism
2.4.2 Successful models
2.4.3 Space representation
2.5 Symbolic musical spaces
2.5.1 Prediction-based
2.5.2 VAE-based
2.6 Conclusion
3 prediction-based framework 
3.1 Introduction
3.2 CNN-LSTM model
3.2.1 Motivations
3.2.2 Architecture
3.2.3 Hierarchical attention modulation
3.2.4 Data and training
3.3 Method evaluation
3.3.1 Prediction results
3.3.2 Embedded data visualization
3.4 Conclusion
4 vae-based framework 
4.1 Introduction
4.2 Motivation
4.3 Polyphonic music representations
4.3.1 The signal-like representation
4.3.2 Benchmark
4.4 Spaces evaluation
4.4.1 Musical analysis
4.4.2 Results
4.5 Conclusion
5 applications 
5.1 Introduction
5.2 Composers classification
5.2.1 Settings
5.2.2 Discussion
5.3 Creativity support tool
5.3.1 Attribute vector arithmetic
5.3.2 Interpolation
5.3.3 Discussion
5.4 Conclusion
6 conclusion 
6.1 Summary and discussion
6.2 Future works
6.3 Overall conclusion
bibliography