Understanding Music With Ai

This anthology provides an informative and timely introduction to ongoing research on music as a cognitive process, bringing a new coherence to the emerging science of musical activity. Following the foreword, which is based on a conversation with Marvin Minsky, 26 contributions explore musical composition, analysis, performance, perception, and learning and tutoring. Their goal is to discover how these activities can be interpreted, understood, modeled, and supported through the use of computer programs. Each chapter is put into perspective by the editors, and empirical investigations are framed by a discussion of the nature of cognitive musicology and of epistemological problems of modeling musical action. The contributions, drawn from two international workshops on AI and Music held in 1988 and 1989, are grouped in seven sections. Topics in these sections take up two views of the nature of cognitive musicology (Kugel, Laske), principles of modeling musical activity (Balaban, Bel, Blevis, Glasgow and Jenkins, Courtot, Smoliar), approaches to music composition (Ames and Domino, Laske, Marsella, Riecken), music analysis by synthesis (Cope, Ebcioglu, Maxwell), realtime performance of music (Bel and Kippen, Ohteru and Hashimoto), music perception (Desain and Honing, Jones, Miller and Scarborough, Linster), and learning/tutoring (Baker, Widmer).

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 research fields of "artificial intelligence and music" and "cognitive musicology" are relative newcomers to the many interdisciplinary groupings based around the centre of AI and cognitive science. They are concerned with the computational study and emulation of human behaviour with respect to music, in many aspects, and with varying degrees of emphasis on psychological plausibility. Recent publications have included work in such diverse areas as rhythm and pitch perception, performance, composition, and formal analysis. Music shares with language the property of giving access to human mental behaviour in a very direct way. As such, it has the potential to be a very useful domain for AI work. Furthermore, in the course of time, AI related work will surely throw light back onto some or all of the fields to which it is applied. Indeed, we are already beginning to feel the benefits of the application of AI techniques to music technology. It is not surprising, therefore, that one of the first areas interest for of musical AI study is that of music education. There are many ways in which an artificial intelligence or cognitive science approach to music education may be applied - for example, to automate tuition, to explain learning processes, to provide metaphors for human computer interaction, and so on. This collection of papers, which is intended to give an impression of both the breadth and depth of the field, originated from a workshop entitled "Music Education: An Artificial Intelligence Approach".

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.

The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). This third volume is dedicated to the interfaces of AI with various fields, with which strong links exist either at the methodological or at the applicative levels. The foreword of this volume reminds us that AI was born for a large part from cybernetics. Chapters are devoted to disciplines that are historically sisters of AI: natural language processing, pattern recognition and computer vision, and robotics. Also close and complementary to AI due to their direct links with information are databases, the semantic web, information retrieval and human-computer interaction. All these disciplines are privileged places for applications of AI methods. This is also the case for bioinformatics, biological modeling and computational neurosciences. The developments of AI have also led to a dialogue with theoretical computer science in particular regarding computability and complexity. Besides, AI research and findings have renewed philosophical and epistemological questions, while their cognitive validity raises questions to psychology. The volume also discusses some of the interactions between science and artistic creation in literature and in music. Lastly, an epilogue concludes the three volumes of this Guided Tour of AI Research by providing an overview of what has been achieved by AI, emphasizing AI as a science, and not just as an innovative technology, and trying to dispel some misunderstandings.

Amid the recent increase in scholarly attention to rock music, Understanding Rock stands out as one of the first books that subjects diverse aspects of the music itself to close and sophisticated analytical scrutiny. Written by some of the best young scholars in musicology and music theory, the essays in this volume use harmonic, melodic, rhythmic, formal, and textual approaches in order to show how and why rock music works as music. Topics of discussion include the adaptation of blues and other styles to rock; the craft of songwriting; techniques and strategies of improvisation; the reinterpretation of older songs; and the use of the recording studio as a compositional tool. A broad range of styles and groups is covered, including Yes, the Beach Boys, Cream, k.d. lang, Paul Simon, Jimi Hendrix, and the Grateful Dead.

Artificial Intelligence (AI) fascinates, challenges and disturbs us. There are many voices in society that predict drastic changes that may come as a consequence of AI – a possible apocalypse or Eden on earth. However, only a few people truly understand what AI is, what it can do and what its limitations are. Understanding Artificial Intelligence explains, through a straightforward narrative and amusing illustrations, how AI works. It is written for a non-specialist reader, adult or adolescent, who is interested in AI but is missing the key to understanding how it works. The author demystifies the creation of the so-called "intelligent" machine and explains the different methods that are used in AI. It presents new possibilities offered by algorithms and the difficulties that researchers, engineers and users face when building and using such algorithms. Each chapter allows the reader to discover a new aspect of AI and to become fully aware of the possibilities offered by this rich field.