Existing approaches on polyphonic music generation

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Table of contents

1 introduction
1.1 Motivations
1.2 Contributions
i overview
2 musical symbolic data
2.1 Symbolic music notation formats
2.1.1 The modern Western musical notation format .
2.1.2 Markup languages
2.1.3 ABC notation
2.1.4 MIDI
2.2 Singularities of the symbolic musical data
2.2.1 Melody, harmony and rhythm
2.2.2 Structure, motives and patterns
2.2.3 Style
2.3 Symbolic Music Datasets
2.3.1 Monophonic datasets
2.3.2 Polyphonic datasets
2.3.3 MIDI file collections
2.3.4 The Chorale Harmonizations by J.S. Bach
3 challenges in music generation
3.1 Building representations
3.1.1 Notes
3.1.2 Rhythm
3.1.3 Melodico-rhythmic encoding
3.2 Complexity of the musical data
3.3 Evaluation of generative models
3.4 Generative models for music, what for?
4 deep learning models for symbolic music generation
4.1 Sequential Models
4.1.1 Models on monophonic datasets
4.1.2 Polyphonic models
4.2 Autoencoder-based approaches
4.2.1 Variational Autoencoder for MIDI generation .
ii polyphonic music modeling
5 style imitation and chord invention in polyphonic music with exponential families
5.1 Introduction
5.2 Existing approaches on polyphonic music generation .
5.3 The model
5.3.1 Description of the model
5.3.2 Training
5.3.3 Generation
5.4 Experimental Results
5.4.1 Style imitation
5.4.2 Chord Invention
5.4.3 Higher-order interactions
5.4.4 Flexibility
5.4.5 Impact of the regularization parameter
5.4.6 Rhythm
5.5 Discussion and future work
6 deepbach: a steerable model for bach chorales generation
6.1 Introduction
6.2 DeepBach
6.2.1 Data Representation
6.2.2 Model Architecture
6.2.3 Generation
6.2.4 Implementation Details
6.3 Experimental Results
6.3.1 Setup
6.3.2 Discrimination Test: the “Bach or Computer” experiment
6.4 Interactive composition
6.4.1 Description
6.4.2 Adapting the model
6.4.3 Generation examples
6.5 Discussion and future work
iii novel techniques in sequence generation
7 deep rank-based transposition-invariant distances on musical sequences
7.1 Introduction
7.2 Related works
7.3 Corpus-based distance
7.3.1 Rank-based distance
7.3.2 Sequence-to-Sequence autoencoder
7.3.3 ReLU non-linearity and truncated Spearman rho distance
7.4 Transformation-invariant distances
7.5 Experimental results
7.5.1 Implementation details
7.5.2 Nearest neighbor search
7.5.3 Invariance by transposition
7.6 Conclusion
8 interactive music generation with unary constraints using anticipation-rnns
8.1 Introduction
8.2 Statement of the problem
8.3 The model
8.4 Experimental results
8.4.1 Dataset preprocessing
8.4.2 Implementation details
8.4.3 Enforcing the constraints
8.4.4 Anticipation capabilities
8.4.5 Sampling with the correct probabilities
8.4.6 Musical examples
8.5 Conclusion
9 glsr-vae: geodesic latent space regularization for variational autoencoder architectures
9.1 Introduction
9.2 Regularized Variational Autoencoders
9.2.1 Background on Variational Autoencoders
9.2.2 Geodesic Latent Space Regularization (GLSR) .
9.3 Experiments
9.3.1 VAEs for Sequence Generation
9.3.2 Data Preprocessing
9.3.3 Experimental Results
9.4 Implementation Details
9.5 Choice of the regularization parameters
9.6 Discussion and Conclusion
a examples of generated music
a.1 Non interactive generations
a.2 Interactive generations
b résumé de la thèse
b.1 Introduction
b.2 Contributions
b.3 Données musicales, challenges et critique de l’état de l’art
b.3.1 Données musicales symboliques
b.3.2 Les challenges de la génération de musique symbolique
b.3.3 Les modèles génératifs profonds pour la musique symbolique
b.4 Modélisation de la musique polyphonique
b.4.1 Familles exponentielles pour l’imitation du style et l’invention d’accords dans la musique polyphonique
b.4.2 DeepBach: un modèle contrôlable pour la génération de chorals
b.5 Techniques nouvelles pour la génération séquentielle .
b.5.1 Distances de rang invariantes par transposition pour des séquences musicales
b.5.2 Anticipation-RNN: Génération interactive de musique sujette à des contraintes unaires
b.5.3 GLSR-VAE: Régularisation géodésique de l’espace latent pour les auto-encodeurs variationnels
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