Dissertation/Thesis Abstract

Algorithmic music analysis: A case study of a prelude from David Cope's "From Darkness, Light"
by Kramer, Reiner, Ph.D., University of North Texas, 2015, 453; 10034328
Abstract (Summary)

The use of algorithms in compositional practice has been in use for centuries. With the advent of computers, formalized procedures have become an important part of computer music. David Cope is an American composer that has pioneered systems that make use of artificial intelligence programming techniques. In this dissertation one of David Cope’s compositions that was generated with one of his processes is examined in detail. A general timeline of algorithmic compositional practice is outlined from a historical perspective, and realized in the Common Lisp programming language as a musicological tool. David Cope’s compositional output is summarized with an explanation of what types of systems he has utilized in the analyses of other composers’ music, and the composition of his own music.

Twentieth century analyses techniques are formalized within Common Lisp as algorithmic analyses tools. The tools are then combined with techniques developed within other computational music analyses tools, and applied toward the analysis of Cope’s prelude. A traditional music theory analysis of the composition is provided, and outcomes of computational analyses augment the traditional analysis. The outcome of the computational analyses, or algorithmic analyses, is represented in statistical data, and corresponding probabilities. From the resulting data sets part of a machine-learning technique algorithm devises semantic networks. The semantic networks represent chord succession and voice leading rules that underlie the framework of Cope’s prelude.

Indexing (document details)
Advisor: Bard-Schwarz, David
Commitee:
School: University of North Texas
Department: Music History, Theory, and Ethnomusicology
School Location: United States -- Texas
Source: DAI-A 77/08(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Music, Information Technology, Computer science
Keywords: Composition, Computational music analysis, Machine learning, Music theory, Semantic networks
Publication Number: 10034328
ISBN: 9781339536248
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