Artificial intelligence in music, sound, art and design : 9th International Conference, EvoMUSART 2020, held as part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings / Juan Romero, Anikó Ekárt, Tiago Martins, João Correia, editors.

International conference proceedings.

Print version record.

Intro -- Preface -- Organization -- Contents -- A Deep Learning Neural Network for Classifying Good and Bad Photos -- 1 Introduction -- 1.1 Research Questions -- 2 Related Work -- 2.1 Aesthetics and Photography -- 2.2 Aesthetic Neural Network Architecture -- 2.3 Deep Learning and Feature Extraction Approaches -- 3 Baseline Architecture -- 3.1 Objective Function, Optimizer and Hyper Parameters -- 3.2 Dataset and Training Setup -- 4 Image Pre-processing Algorithm and Aesthetics Assessment Accuracy -- 4.1 Image Pre-processing and Unambiguously Rated Photos

4.2 Image Pre-processing and Ambiguously Rated Photos -- 4.3 NIMA Model Comparison -- 5 Salience and Aesthetic Assessment Accuracy -- 5.1 Salience Algorithms and Unambiguously Rated Photos -- 5.2 Salience Algorithms and Ambiguously Rated Photos -- 5.3 NIMA Model Comparison -- 6 Conclusion -- 6.1 Further Work -- References -- Adapting and Enhancing Evolutionary Art for Casual Creation -- 1 Introduction -- 2 A Fun-First Design Methodology -- 3 Image Generation Efficiency -- 4 Machine Vision Enhancements -- 4.1 Deriving a Machine Vision Setup -- 4.2 Deploying the Vision Model in Art Done Quick

5 Related Work -- 6 Conclusions and Future Work -- References -- Comparing Fuzzy Rule Based Approaches for Music Genre Classification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Random Forest -- 3.2 Complete Search for Primitive Rules -- 3.3 Fuzzy Pattern Trees -- 3.4 Evolutionary Approach -- 4 Experiments -- 4.1 Setup -- 4.2 Results -- 5 Conclusion -- References -- Quantum Zentanglement: Combining Picbreeder and Wave Function Collapse to Create Zentangles® -- 1 Introduction -- 2 Related Work -- 2.1 Art via Compositional Pattern Producing Networks

2.2 Procedural Content Generation with Wave Function Collapse -- 2.3 Zentangle -- 3 Methods -- 3.1 Compositional Pattern Producing Networks -- 3.2 Wave Function Collapse -- 3.3 Creating Zentangles -- 3.4 Interactive Evolution via Selective Breeding -- 3.5 Automated Evolution with NSGA-II -- 4 Results -- 4.1 Interactively Generated Art -- 4.2 Automatically Generated Art -- 5 Discussion and Future Work -- 6 Conclusion -- References -- Emerging Technology System Evolution -- Abstract -- 1 Introduction -- 1.1 Overview -- 1.2 Prior Work -- 2 Representation -- 3 Interactive Evolution

3.1 Initial Populations -- 3.2 Mutation -- 3.3 Mating -- 3.4 Evolving Populations -- 4 Next Steps -- References -- Fusion of Hilbert-Huang Transform and Deep Convolutional Neural Network for Predominant Musical Instruments Recognition -- Abstract -- 1 Introduction -- 2 The Proposed Method -- 2.1 Hilbert-Huang Transform -- 2.2 Architecture of DCNN -- 3 Experiments -- 3.1 Key Parameters -- 3.2 Comparison with Other Methods -- 4 Conclusions -- Acknowledgements -- References -- Genetic Reverb: Synthesizing Artificial Reverberant Fields via Genetic Algorithms -- 1 Introduction -- 2 Background

2.1 Impulse Response of a Box-Shaped Room

This book constitutes the refereed proceedings of the 9th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EuroGP, EvoCOP and EvoApplications. The 15 revised full papers presented were carefully reviewed and selected from 31 submissions. The papers cover a wide spectrum of topics and application areas, including generative approaches to music and visual art, deep learning, and architecture.