Artificial Intelligence in Music, Sound, Art and Design (eBook, PDF)

9th International Conference, EvoMUSART 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings
Redaktion: Romero, Juan; Correia, João; Martins, Tiago; Ekárt, Anikó

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 227
• Erscheinungstermin: 9. April 2020
• Englisch
• ISBN-13: 9783030438593
• Artikelnr.: 59177214

Inhaltsangabe

A deep learning neural network for classifying good and bad photos.- Adapting and Enhancing Evolutionary Art for Casual Creation.- Comparing Fuzzy Rule Based Approaches for Music Genre Classification.- Quantum Zentanglement: Combining Picbreeder and Wave Function Collapse to Create Zentangles.- Emerging Technology System Evolution.- Fusion of Hilbert-Huang Transform and Deep Convolutional Neural Network for Predominant Musical Instruments Recognition.- Genetic Reverb: Synthesizing Artificial Reverberant Fields Via Genetic Algorithms.- Portraits of No One: An Interactive Installation.- Understanding Aesthetic Evaluation with Deep Learning.- An Aesthetic-Based Fitness Measure and a Framework for Guidance of Evolutionary Design in Architecture.- Objective Evaluation of Tonal Fitness for Chord Progressions.- Coevolving Artistic Images Using OMNIREP.- Sound Cells in Genetic Improvisation: An Evolutionary Model for Improvised Music.- Controlling Self-Organization in Generative Creative Systems.- Emulation Games. See and Be Seen, a Subjective Approach to Analog Computational Neuroscience.