Machine Models of MusicStephan M. Schwanauer, David A. Levitt Machine Models of Music brings together representative models and current research to illustrate the rich impact that artificial intelligence has had on the understanding and composition of traditional music and to demonstrate the ways in which music can push the boundaries of traditional Al research. Machine Models of Music brings together representative models ranging from Mozart's "Musical Dice Game" to a classic article by Marvin Minsky and current research to illustrate the rich impact that artificial intelligence has had on the understanding and composition of traditional music and to demonstrate the ways in which music can push the boundaries of traditional Al research.Major sections of the book take up pioneering research in generate-and-test composition (Lejaren Hiller, Barry Brooks, Jr., Stanley Gill); composition parsing (Allen Forte, Herbert Simon, Terry Winograd); heuristic composition (John Rothgeb, James Moorer, Steven Smoliar); generative grammars (Otto Laske, Gary Rader, Johan Sundberg, Fred Lerdahl); alternative theories (Marvin Minsky, James Meehan); composition tools (Charles Ames, Kemal Ebcioglu, David Cope, C. Fry); and new directions (David Levitt, Christopher Longuet-Higgins, Jamshed Bharucha, Stephan Schwanauer).Stephan Schwanauer is President of Mediasoft Corporation. David Levitt is the founder of HIP Software and head of audio products at VPL Research. |
Contents
Introduction | 1 |
4 | 55 |
5 | 63 |
6 | 94 |
III | 115 |
7 | 157 |
Process Structuring and Music Theory | 189 |
10 | 215 |
Motivation Design and Production of | 357 |
An Expert System for Harmonizing FourPart Chorales | 385 |
A Computer Model of Music Composition | 403 |
A Language for Specifying Musical Style | 427 |
A Representation for Musical Dialects | 455 |
The Perception of Melodies | 471 |
A Connectionist Model of Musical Harmony | 497 |
A Learning Machine for Tonal Composition | 511 |