Machine models of music
著者
書誌事項
Machine models of music
MIT Press, c1993
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
- Part 1 Foundations - generate-and-test composition: musical composition with a high-speed digital computer, Lejaren Hiller and Leonard Isaacson
- an experiemnt in musical composition, F.P. Brooks et al
- a technique for the composition of music in a computer, Stanley Gill. Part 2 Foundations - composition parsing: a programme for the analytical reading of scores, Allen Forte
- pattern in music, Herbert A. Simon and Richard K. Sumner
- linguistics and the computer analysis of tonal harmony, Terry Winograd. Part 3 AI and music - heuristic composition: simulating musical skills by digital computer, John Rothgeb
- music and computer composition, James Anderson Moorer
- process structuring and music theory, Stephen W. Smoliar. Part 4 AI and music - generative grammars: in search of a generative grammar for music, Otto E. Laske
- a method for composing simple traditional music by computer, Gary M. Rader
- generative theories in language and music descriptions, Johan Sundberg and Bjorn Lindblom
- an overview of hierarchical structure in music, Fred Lerdahl and Ray Jeckandoff. Part 5 AI and music - alternative theories: an artificial intelligence approach to tonal music theory, James Meehan
- music, mind and meaning, Marvin Minsky. Part 6 AI and music - composition tools: "protocol" - motivation, design and production of a composition for solo piano, Charles Ames
- an expert system for harmonizing four-part chorales, Kemal Ebcioglu
- a computer model of music composition, David Cope
- flavours band - a language for specifying musical style, Christopher Fry. Part 7 AI and music - new directions: a representation for musical dialects, David A. Levitt
- the perception of melodies, H. Christopher Longuet-Higgins
- MUSACT - a connectionist model of musical harmony, Jamshed J. Bharucha
- a learning machine for tonal composition, Stephan M. Schwanauer. Appendix: musical dice game, Wolfgang Amadeus Mozart.
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