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

8th International Conference, EvoMUSART 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24-26, 2019, Proceedings
Redaktion: Ekárt, Anikó; Castro Pena, María Luz; Liapis, Antonios

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Computation in Combinatorial Optimization, EvoMUSART 2019, held in Leipzig, Germany, in April 2019, co-located with the Evo*2019 events EuroGP, EvoCOP and EvoApplications.

The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The papers cover a wide range of topics and application areas, including: visual art and music generation, analysis, and interpretation; sound synthesis; architecture; video; poetry; design; and other creative tasks.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 249
• Erscheinungstermin: 10. April 2019
• Englisch
• ISBN-13: 9783030166670
• Artikelnr.: 57073331

Inhaltsangabe

Deep Learning Concepts for Evolutionary Art.- Adversarial Evolution and Deep Learning - How Does An Artist Play with Our Visual System.- Autonomy, Authenticity, Authorship and Intention in Computer Generated Art.- Camera Obscurer: Generative Art for Design Inspiration.- Swarm-Based Identification of Animation Key Points from 2D-medialness Maps.- Paintings, Polygons and Plant Propagation.- Evolutionary Games for Audiovisual Works: Exploring the Demographic Prisoner's Dilemma.- Emojinating: Evolving Emoji Blends.- Automatically Generating Engaging Presentation Slide Decks.- Tired of choosing? Just Add Structure and Virtual Reality.- EvoChef: Show Me What to Cook! Artificial Evolution of Culinary Arts.- Comparing Models for Harmony Prediction in An Interactive Audio Looper.- Stochastic Synthesizer Patch Exploration in Edisyn.- Evolutionary Multi-Objective Training Set Selection of Data Instances and Augmentations for Vocal Detection.- Automatic Jazz Melody Composition Through a Learning-Based Genetic Algorithm.- Exploring Transfer Functions in Evolved CTRNNs for Music Generation.