This book constitutes the refereed proceedings of the 10th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2022, held as part of Evo* 2022, in April 2022, co-located with the Evo* 2022 events, EvoCOP, EvoApplications, and EuroGP. The 20 full papers and 6 short papers presented in this book were carefully reviewed and selected from 66 submissions. They cover a wide range of topics and application areas, including generative approaches to music and visual art, deep learning, and architecture.

This book constitutes the refereed proceedings of the 12th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2023, held as part of Evo* 2023, in April 2023, co-located with the Evo* 2023 events, EvoCOP, EvoApplications, and EuroGP. The 20 full papers and 7 short papers presented in this book were carefully reviewed and selected from 55 submissions. They cover a wide range of topics and application areas of artificial intelligence, including generative approaches to music and visual art, deep learning, and architecture.

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.

This book constitutes the refereed proceedings of the 10th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2021, held as part of Evo* 2021, as Virtual Event, in April 2021, co-located with the Evo* 2021 events, EvoCOP, EvoApplications, and EuroGP. The 24 revised full papers and 7 short papers presented in this book were carefully reviewed and selected from 66 submissions. They cover a wide range of topics and application areas, including generative approaches to music and visual art, deep learning, and architecture.

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.

This book constitutes the refereed proceedings of the 6th International Conference on Evolutionary Computation in Combinatorial Optimization, EvoMUSART 2017, held in Amsterdam, The Netherlands, in April 2017, co-located with the Evo*2017 events EuroGP, EvoCOP and EvoApplications. The 24 revised full papers presented were carefully reviewed and selected from 29 submissions. The papers cover a wide range of topics and application areas, including: generative approaches to music, graphics, game content, and narrative; music information retrieval; computational aesthetics; the mechanics of interactive evolutionary computation; computer-aided design; and the art theory of evolutionary computation.

AI-Generated Content (AIGC) is a revolutionary engine for digital content generation. In the area of art, AI has achieved remarkable advancements. AI is capable of not only creating paintings or music comparable to human masterpieces, but it also understands and appreciates artwork. For professionals and amateurs, AI is an enabling tool and an opportunity to enjoy a new world of art. This book aims to present the state-of-the-art AI technologies for art creation, understanding, and evaluation. The contents include a survey on cross-modal generation of visual and auditory content, explainable AI and music, AI-enabled robotic theater for Chinese folk art, AI for ancient Chinese music restoration and reproduction, AI for brainwave opera, artistic text style transfer, data-driven automatic choreography, Human-AI collaborative sketching, personalized music recommendation and generation based on emotion and memory (MemoMusic), understanding music and emotion from the brain, music question answering, emotional quality evaluation for generated music, and AI for image aesthetic evaluation. The key features of the book are as follows: AI for Art is a fascinating cross-disciplinary field for the academic community as well as the public. Each chapter is an independent interesting topic, which provides an entry for corresponding readers. It presents SOTA AI technologies for art creation and understanding. The artistry and appreciation of the book is wide-ranging – for example, the combination of AI with traditional Chinese art. This book is dedicated to the international cross-disciplinary AI Art community: professors, students, researchers, and engineers from AI (machine learning, computer vision, multimedia computing, affective computing, robotics, etc.), art (painting, music, dance, fashion, design, etc.), cognitive science, and psychology. General audiences can also benefit from this book.

An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.

This book constitutes the refereed proceedings of the Third International Conference on Biologically Inspired Music, Sound, Art and Design, Evo MUSART 2014, held in Granada, Spain, in April 2014, co-located with the Evo* 2013 events Euro GP, Evo COP, Evo BIO and Evo Applications. The 11 revised full papers presented were carefully reviewed and selected from 30 submissions. They cover a wide range of topics and application areas.

Emotions, creativity, aesthetics, artistic behavior, divergent thoughts, and curiosity are both fundamental to the human experience and instrumental in the development of human-centered artificial intelligence systems that can relate, communicate, and understand human motivations, desires, and needs. In this book the editors put forward two core propositions: creative artistic behavior is one of the key challenges of artificial intelligence research, and computer-assisted creativity and human-centered artificial intelligence systems are the driving forces for research in this area. The invited chapters examine computational creativity and more specifically systems that exhibit artistic behavior or can improve humans' creative and artistic abilities. The authors synthesize and reflect on current trends, identify core challenges and opportunities, and present novel contributions and applications in domains such as the visual arts, music, 3D environments, and games. The book will be valuable for researchers, creatives, and others engaged with the relationship between artificial intelligence and the arts.