Abstract
There are several automatic music composition algorithms that generate a classical music melody or Jazz and Blues chords and progressions. These algorithms often work by applying a series of music rules which are explicitly provided in order to determine the sequence of the exit codes. In other cases, the algorithms use generation techniques based on Markov Models. The objective of this article is to present an algorithm for the automatic composition of classical tonal music, based on a self-learning model that combines De La Motte’s theory of Functional Harmony in a Markov process. This approach has the advantage of being more general compared to the explicit specification of rules. The article is going to demonstrate the effectiveness of the method by means of some examples of its production and is going to indicate ways to improve the method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cambouropoulos, E.: Markov chains as an aid to computer-assisted composition. Musical Prax. 1(1), 41–52 (1994)
Wadi, A.: Analysis of music note patterns via markov chains, senior honors projects. Paper 2 (2012)
Lichtenwalter, R.N., Lichtenwalter, K., Chawla, N.V.: Applying learning algorithms to music generation. In: Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009
Cope, D.: Experiments in Musical Intelligence. A-R Editions Inc., Madison, WI (1996)
Chan, M., Potter, J., Schubert, E.: Improving algorithmic music composition with machine learning. In: Proceedings of the 9th International Conference on Music Perception and Cognition, ICMPC 2006
Lerdahl, F., Jackendoff, R.: A Generative Theory of Tonal Music. Cambridge: MIT Press (1983)
Moroni, A., Manzolli, J., Van Zuben, F., Gudwin, R., Populi, V.: An interactive evolutionary system for algorithmic music composition. Leonardo Music J 10, 49–54 (2000)
Thom, B.: An interactive improvisational music companion, In: Proceeding of the Fourth International Conference on Autonomous Agents, pp 309–316. Barcelona, Spain (2000)
de la Motte, D.: Manuale di armonia, Bärenreiter (1976)
Coltro, B.: Lezioni di armonia complementare, Zanibon (1979)
Bengio, Y.: Markovian models for sequential data. Neural Comput Surv 2, 129–162 (1999)
Bini, D.A., Latouche, G., Meini, B.: Numerical Methods for Structured Markov Chains. Oxford University Press (2005)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)
Kazi, N., Bhatia, S.: Various artificial intelligence techniques for automated melody generation. Int J Eng Res Technol 2, 1646–1652 (2013)
Della Ventura, M.: Influence of the harmonic/functional analysis on the musical execution: representation and algorithm. In: Proceedings of the International Conference on Applied Informatics and Computing Theory, Barcellona (Spain) (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Ventura, M.D. (2016). Automatic Music Composition from a Self-learning Algorithm. In: Ravulakollu, K., Khan, M., Abraham, A. (eds) Trends in Ambient Intelligent Systems. Studies in Computational Intelligence, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-319-30184-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-30184-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30182-2
Online ISBN: 978-3-319-30184-6
eBook Packages: EngineeringEngineering (R0)