Search
Abstract
The paper concerns an original evolutionary music composition system. On the basis of available solutions, we have selected a finite set of music features which appear to have a key impact on the quality of composed musical phrases. Evaluation criteria have been divided into rule-based and statistical sub-sets. Elements of the cost function are modeled using a Gaussian distribution defined by the expected value and variance obtained from an analysis of recognized music pieces. An evolutionary algorithm, considering a reference sequence of chords as an input, is created, implemented and tested. The results of a sampling survey (poll) proves that the melodies generated by the system arouse the interest of a listener.
Citations
-
0
CrossRef
-
0
Web of Science
-
0
Scopus
Authors (3)
Cite as
Full text
- Publication version
- Accepted or Published Version
- License
- Copyright (Springer International Publishing AG 2017)
Keywords
Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Published in:
-
Advances in Intelligent Systems and Computing
no. 577,
pages 722 - 733,
ISSN: 2194-5357 - Title of issue:
- Trends in Advanced Intelligent Control, Optimization and Automation strony 722 - 733
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Kowalczuk Z., Tatara M. S., Bąk A.: Evolutionary music composition system with statistically modeled criteria// / ed. Wojciech Mitkowski, Janusz Kacprzyk, Krzysztof Oprzędkiewicz, Paweł Skruch Cham: Springer, 2017, s.722-733
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-60699-6_70
- Bibliography: test
-
show (8) hide
- Fernández J. D., Vico F.: AI methods in algorithmic composition: A comprehensive survey. Journal of Artificial Intelligence Research 48, 513-582 (2013). open in new tab
- Biles J.: GenJam: A genetic algorithm for generating jazz solos. ICMC Proceedings, 131-137 (1994). open in new tab
- Liu C., Ting C.: Evolutionary composition using music theory and charts. IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC), 63-70 (2013). open in new tab
- Manaris B., Vaughan D., Wagner C., Romero J., Davis R. B.: Evolutionary music and the Zipf-Mandelbrot Law: Developing fitness functions for pleasant music. Ap- plications of Evolutionary Computing, 522-534, Springer, Berlin-Heidelberg (2003). open in new tab
- Waschka R.: Avoiding the fitness "bottleneck": Using genetic algorithms to compose orchestral music. ICMC Proceedings, 201-203 (1999). open in new tab
- Towsey M., Brown A., Wright S., Diederich J.: Towards melodic extension using genetic algorithms. Educational Technology & Society 4(2) 54-65 (2001).
- Wiggins G., Papadopoulos G., Phon-Amnuaisuk S., Tuson A.: Evolutionary meth- ods for musical composition. International Journal of Computing Anticipatory Sys- tems (1999).
- Bradley M. M., Lang P. J. Measuring emotion: the Self-Assessment Manikin and the Semantic Differential, Journal of behavior therapy and experimental psychiatry, 25(1) pp. 49-59, 1994. open in new tab
- Verified by:
- Gdańsk University of Technology
seen 190 times