Buch, Englisch, 227 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 371 g
9th International Conference, EvoMUSART 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings
Buch, Englisch, 227 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 371 g
Reihe: Theoretical Computer Science and General Issues
ISBN: 978-3-030-43858-6
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.
