Intelligent Music Production presents the state of the art in approaches, methodologies and systems from the emerging field of automation in music mixing and mastering. This book collects the relevant works in the domain of innovation in music production, and orders them in a way that outlines the way forward: first, covering our knowledge of the music production processes; then by reviewing the methodologies in classification, data collection and perceptual evaluation; and finally by presenting recent advances on introducing intelligence in audio effects, sound engineering processes and music production interfaces. Intelligent Music Production is a comprehensive guide, providing an introductory read for beginners, as well as a crucial reference point for experienced researchers, producers, engineers and developers.
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.
Distortion in Music Production offers a range of valuable perspectives on how engineers and producers use distortion and colouration as production tools. Readers are provided with detailed and informed considerations on the use of non-linear signal processing, by authors working in a wide array of academic, creative, and professional contexts. Including comprehensive coverage of the process, as well as historical perspectives and future innovations, this book features interviews and contributions from academics and industry practitioners. Distortion in Music Production also explores ways in which music producers can implement the process in their work and how the effect can be used and abused through examination from technical, practical, and musicological perspectives. This text is one of the first to offer an extensive investigation of distortion in music production and constitutes essential reading for students and practitioners working in music production.
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.
Innovation in Music: Technology and Creativity is a groundbreaking collection bringing together contributions from instructors, researchers, and professionals. Split into two sections, covering composition and performance, and technology and innovation, this volume offers truly international perspectives on ever-evolving practices. Including chapters on audience interaction, dynamic music methods, AI, and live electronic performances, this is recommended reading for professionals, students, and researchers looking for global insights into the fields of music production, music business, and music technology.
The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, pattern recognition, image processing, bioinformatics, and computational biology. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the m- tifaceted aspects of intelligent computing. ICIC 2010, held in Changsha, China, August 18–21, 2010, constituted the 6th - ternational Conference on Intelligent Computing. It built upon the success of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005, that were held in Ulsan, Korea, Shanghai, Qingdao, Kunming, and Hefei, China, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced Intelligent Computing Technology and Applications.” Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
The philosopher Spinoza once asserted that no one knows what a body can do, conceiving an intrinsic bodily power with unknown limits. Similarly, we can ask ourselves about Artificial Intelligence (AI): To what extent is the development of intelligence limited by its technical and material substrate? In other words, what can AI do? The answer is analogous to Spinoza’s: Nobody knows the limit of AI. Critically considering this issue from philosophical, interdisciplinary, and engineering perspectives, respectively, this book assesses the scope and pertinence of AI technology and explores how it could bring about both a better and more unpredictable future. What AI Can Do highlights, at both the theoretical and practical levels, the cross-cutting relevance that AI is having on society, appealing to students of engineering, computer science, and philosophy, as well as all who hold a practical interest in the technology.
This book presents comprehensive coverage of the latest advances in research into enabling machines to listen to and compose new music. It includes chapters introducing what we know about human musical intelligence and on how this knowledge can be simulated with AI. The development of interactive musical robots and emerging new approaches to AI-based musical creativity are also introduced, including brain–computer music interfaces, bio-processors and quantum computing. Artificial Intelligence (AI) technology permeates the music industry, from management systems for recording studios to recommendation systems for online commercialization of music through the Internet. Yet whereas AI for online music distribution is well advanced, this book focuses on a largely unexplored application: AI for creating the actual musical content.
Automatic music composition has blossomed with the introduction of intelligent methodologies in computer science. Thereby, many methodologies for automatic music composition have been or could be described as “intelligent,” but what exactly is it that makes them intelligent? Furthermore, is there any categorization of intelligent music composition (IMC) methodologies that is both consistent and descriptive? This chapter aims to provide some insights on what IMC methodologies are, through proposing and analyzing a detailed categorization of them. Toward this perspective, methodologies that incorporate bioinspired intelligent algorithms (such as cellular automata, L-systems, genetic algorithms, swarm intelligence, among others) as well as their combinations are considered and briefly reviewed. At the same time, a consistent categorization of these methodologies is proposed, taking into account the utilization of their intelligent algorithm in accordance to their overall compositional aims. To this end, three main categories can be defined: the “unsupervised,” the “supervised,” and the “interactive” IMC methodologies.
