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Table of contents
1 introduction
1.1 Context – Electronic Dance Music
1.1.1 A definition
1.1.2 History and taxonomy
1.1.3 Electronic/Dance Music Musical Characteristics
1.2 Dissertation Organization and main contributions
2 fundamentals and state of the art
2.1 Introduction
2.2 Core Definitions
2.2.1 Rhythm
2.2.2 Musical genres
2.3 Handcrafted systems
2.3.1 Signal Fundamentals
2.3.2 Rhythm handcrafted features
2.3.3 Musical genre handcrafted features
2.4 Data-driven systems
2.4.1 Machine learning fundamentals
2.4.2 Data-driven tempo estimation
2.4.3 Data-driven genre classification
2.5 Electronic/Dance Music in Music Information Retrieval
2.6 Conclusion
3 datasets
3.1 Introduction
3.2 Commonly used datasets
3.3 Electronic Dance Music Datasets
3.4 Discussion
4 deep rhythm
4.1 Introduction
4.2 Motivations
4.2.1 Harmonic representation of rhythm components
4.2.2 Adaptation to a deep learning formalism
4.3 Harmonic Constant-Q Modulation
4.3.1 Computation
4.3.2 Visual identification of tempo
4.4 Deep Convolutional Neural network
4.4.1 Architecture of the Convolutional Neural Network
4.4.2 Training
4.5 Aggregating decisions over time
4.5.1 Oracle Frame Prediction
4.5.2 Attention Mecanism
4.6 Evaluation
4.6.1 Tempo Estimation
4.6.2 Rhythm-oriented genre classification
4.7 Conclusion
5 deep rhythm extensions
5.1 Introduction
5.2 Complex Deep Rhythm
5.2.1 Why complex representation/convolution?
5.2.2 Complex HCQM.
5.2.3 Complex Convolution
5.2.4 Evaluation
5.3 Multitask Learning
5.3.1 Why multitask learning?
5.3.2 Multitask Deep Rhythm
5.3.3 Evaluation
5.4 Multi-Input network
5.4.1 Why multi-input network?
5.4.2 Multi-input Network
5.4.3 Evaluation
5.5 Conclusion
6 metric learning deep rhythm
6.1 Introduction
6.2 Metric learning principles
6.3 Losses
6.3.1 Evolution of metric learning losses
6.3.2 Triplet loss
6.4 Triplet Loss Deep Rhythm
6.4.1 Architecture
6.4.2 Training
6.5 Evaluation and Analysis
6.5.1 Datasets
6.5.2 Embedding space
6.5.3 Classifiers
6.5.4 Classification Results
6.6 Conclusion
7 conclusion
7.1 Summary and main contributions
7.2 Future Works
7.3 Overall conclusion
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