Machine Models of Music
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.
HardcoverOut of Print ISBN: 9780262193191 556 pp. | 6.1 in x 9.1 in
This welcome anthology presents a family portrait of those who, over a span now of almost forty years, have tried to enable computers to compose music. The music theorists, psychologists, linguists, composers, and computer scientists brought together here all share a fascination with the question, if a computer is to compose, what must it know about music? As this collection of classic essays shows, however, they do not all agree on the answer.
Robert O. Gjerdingen
SUNY at Stony Brook
The book entitled Machine Models of Music edited by Schwanauer and Levitt is a welcome and much needed collection of essentially all the important studies in computer modelling of musical processes from the pathbreaking work of Hiller and Isaacson through Winograd's paper on computer analysis of tonal music and Smoliar's application of process theory in music analysis to an interesting description of a learning machine for tonal composition by Schwanauer. It is particularly useful to have all these papers together in one place, with a general index (!). The collection will prove valuable indeed for all music theorists concerned with the foundation of their discipline.
Professor of Music and Director of the Computer and Electronic Music Studios, University of California