The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of COMPUTERS
Transactions ID Number: 17-285
Full Name: Michele Van Dyne
Position: Associate Professor
Age: ON
Sex: Female
Address: 1300 W. Park St., Butte, MT 59701
Country: UNITED STATES
Tel: 406-496-4855
Tel prefix: 1
Fax: 406-496-4756
E-mail address: mvandyne@mtech.edu
Other E-mails: mvandyne@bresnan.net,nathan.fortier@gmail.com
Title of the Paper: A Genetic Algorithm Approach to Improve Automated Music Composition
Authors as they appear in the Paper: Nathan Fortier, Michele Van Dyne
Email addresses of all the authors: mvandyne@mtech.edu,nathan.fortier@gmail.com
Number of paper pages: 8
Abstract: Using the rules of music theory, a program was written which automatically creates original compositions. These compositions were parameterized by user input concerning preferences on genre, tempo, and tonality. Based on these preferences, initial compositions were generated, and the "best" composition was presented to the user. Following the rules of music theory guarantees that the program produces harmonious compositions, but certain aspects of musical composition cannot be defined by music theory. It is in these aspects of musical composition where the human mind uses creativity. Using the population of compositions initially generated for the user, the program then used a genetic algorithm to evolve compositions that increasingly match the user's preferences, allowing the program to make decisions that cannot be made using music theory alone. The resulting "best" composition of the evolved population was then presented to the user for evaluation. To test the !
effectiveness of this approach, each composition, both initial and final was ranked by subjects on a scale from 1 to 10. Subjects expressed a significant preference for the evolved compositions over initial compositions.
Keywords: Artificial creativity, artificial intelligence, genetic algorithms, machine learning, music
EXTENSION of the file: .doc
Special (Invited) Session: Artificial Creativity: Improving on Algorithmic Music Composition Using Genetic Algorithm
Organizer of the Session: 659-556R
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