Music Education: An Artificial Intelligence Approach

1. Front Matter

   Pages i-ix

   PDF

2. Music Education

   1. Front Matter

      Pages 1-1

      PDF

   2. Agent Reflection in an Intelligent Learning Environment Architecture for Musical Composition

      • John Cook

      Pages 3-23

   3. Learning about harmony with Harmony Space: an overview

      • Simon Holland

      Pages 24-40

   4. Motive

      • Matt Smith, Simon Holland

      Pages 41-55

   5. Uncovering Cognitive Processes in Music Composition: Educational and Computational Approaches

      • Brian K. Smith, H. William Smith Jr.

      Pages 56-73

3. Representation of Musical Knowledge

   1. Front Matter

      Pages 75-75

      PDF

   2. Representations of the MONK harmonisation systems

      • Daran Coates

      Pages 77-91

   3. Slappability: A New Metaphor for Human Computer Interaction

      • Daniel V. Oppenheim

      Pages 92-107

   4. Music Representation — between the Musician and the Computer

      • Alan Smaill, Geraint A. Wiggins, Eduardo Miranda

      Pages 108-119

4. Music Theory and Computational Models

   1. Front Matter

      Pages 121-121

      PDF

   2. Pragmatics in Language and Music

      • Kenny R. Coventry, Tim Blackwell

      Pages 123-142

   3. The Role of Musical Memory in Creativity and Learning: a Study of Jazz Performance

      • Geber Ramalho, Jean-Gabriel Ganascia

      Pages 143-156

   4. Automatic Characterisation of Musical Style

      • Martin D. Westhead, Alan Smaill

      Pages 157-170

5. Back Matter

   Pages 171-173

   PDF

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".

• artificial intelligence
• behavior
• computer
• education
• intelligence
• knowledge
• learning
• memory
• music
• performance

• Department of Computing, King Alfred’s College of Higher Education, Winchester, UK

  Matt Smith

• Department of Artificial Intelligence, University of Edinburgh, Edinburgh, Scotland, UK

  Alan Smaill, Geraint A. Wiggins

Policies and ethics