The interplay between emotional and intellectual elements feature heavily in the research of a variety of scientific fields, including neuroscience, the cognitive sciences and artificial intelligence (AI). This collection of key introductory texts by top researchers worldwide is the first study which introduces the subject of artificial intelligence and music to beginners. Eduardo Reck Miranda received a Ph.D. in music and artificial intelligence from the University of Edinburgh, Scotland. He has published several research papers in major international journals and his compositions have been performed worldwide. Also includes 57 musical examples.

The interplay between emotional and intellectual elements feature heavily in the research of a variety of scientific fields, including neuroscience, the cognitive sciences and artificial intelligence (AI). This collection of key introductory texts by top researchers worldwide is the first study which introduces the subject of artificial intelligence and music to beginners. Eduardo Reck Miranda received a Ph.D. in music and artificial intelligence from the University of Edinburgh, Scotland. He has published several research papers in major international journals and his compositions have been performed worldwide. Also includes 57 musical examples.

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.

The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business. Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.

This book constitutes the refereed proceedings of the Second International Conference on Music and Artificial Intelligence, ICMAI 2002, held in Edinburgh, Scotland, UK in September 2002.The 16 revised full papers presented together with abstracts of 2 invited talks were carefully reviewed and selected for inclusion in the proceedings. Among the topics addressed are parsing for music and language, patterns in music, musical pattern recognition, visualisation, sound classification, tonal structure representation, musical learning systems, pattern analysis, musical perception, melodic segmentation, and time series analysis.

Most artificial intelligence research investigates intelligent behavior for a single agent--solving problems heuristically, understanding natural language, and so on. Distributed Artificial Intelligence (DAI) is concerned with coordinated intelligent behavior: intelligent agents coordinating their knowledge, skills, and plans to act or solve problems, working toward a single goal, or toward separate, individual goals that interact. DAI provides intellectual insights about organization, interaction, and problem solving among intelligent agents. This comprehensive collection of articles shows the breadth and depth of DAI research. The selected information is relevant to emerging DAI technologies as well as to practical problems in artificial intelligence, distributed computing systems, and human-computer interaction. "Readings in Distributed Artificial Intelligence" proposes a framework for understanding the problems and possibilities of DAI. It divides the study into three realms: the natural systems approach (emulating strategies and representations people use to coordinate their activities), the engineering/science perspective (building automated, coordinated problem solvers for specific applications), and a third, hybrid approach that is useful in analyzing and developing mixed collections of machines and human agents working together. The editors introduce the volume with an important survey of the motivations, research, and results of work in DAI. This historical and conceptual overview combines with chapter introductions to guide the reader through this fascinating field. A unique and extensive bibliography is also provided.

Artificial Intelligence and Music Ecosystem highlights the opportunities and rewards associated with the application of AI in the creative arts. Featuring an array of voices, including interviews with Jacques Attali, Holly Herndon and Scott Cohen, this book offers interdisciplinary approaches to pressing ethical and technical questions associated with AI. Considering the perspectives of developers, students and artists, as well as the wider themes of law, ethics and philosophy, Artificial Intelligence and Music Ecosystem is an essential introduction for anyone interested in the impact of AI on music, including those studying and working in the creative arts.