UID:
almahu_9948336372002882
Umfang:
XII, 227 p. 114 illus., 86 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2020.
ISBN:
9783030438593
Serie:
Theoretical Computer Science and General Issues ; 12103
Inhalt:
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.
Anmerkung:
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.
In:
Springer eBooks
Weitere Ausg.:
Printed edition: ISBN 9783030438586
Weitere Ausg.:
Printed edition: ISBN 9783030438609
Sprache:
Englisch
DOI:
10.1007/978-3-030-43859-3
URL:
https://doi.org/10.1007/978-3-030-43859-3
