article

A machine learning approach to two-voice counterpoint composition

Authors:

Kamil Adiloglu,

Ferda N. AlpaslanAuthors Info & Claims

Knowledge-Based Systems, Volume 20, Issue 3

Pages 300 - 309

https://doi.org/10.1016/j.knosys.2006.04.018

Published: 01 April 2007 Publication History

Abstract

Algorithmic composition of musical pieces is one of the most popular areas of computer aided music research. Various attempts have been made successfully in the area of music composition. Artificial intelligence methods have been extensively applied in this area. Representation of musical pieces in a computer-understandable form plays an important role in computer aided music research. This paper presents a neural network-based knowledge representation schema for representing notes, melodies, and time in first species counterpoint pieces. A musical note is composed of pitch and duration in this representation schema. The proposed representation technique was tested using the back-propagation algorithm to generate two-voice counterpoint pieces.

References

[1]

H. Adeli, Knowledge Engineering, vol. 1, Fundamentals, McGraw-Hill, New York, 1990.

Digital Library

[2]

H. Bellermann, Der Kontrapunkt. Hildesheim, Georg Olms Verlag, 2001 (The original was published in 1862).

[3]

Bharucha, J., Pitch, Harmony, and neural nets: a psychological perspective. Music and Connectionism. v2 i4. 84-99.

[4]

D. Cope, Computers and Musical Style, Madison, WI, A-R Editions, 1991.

Digital Library

[5]

D. Cope, Experiments in Musical Intelligence, Madison, WI, A-R Editions, 1996.

[6]

D. Cope, The Algorithmic Composer, Madison, WI, A-R Editions, 2000.

[7]

J. Forner, J. Wilbrandt, Schöpferischer Kontrapunkt, Leipzig, VEB Deutscher Verlag für Musik, 1979.

[8]

J.J. Fux, Gradus ad Parnassum. The Study of Counterpoint: W.W Norton & Company Inc., Vienna, 1725 (Translated and edited by Mann Alfred in 1965).

[9]

Grout, D.J. and Claude, P., A History of Western Music. fourth ed. W.W. Norton & Company, Inc.

[10]

Hild, H.J., Feulner, J. and Menzel, W., Harmonet: a neural net for harmonizing chorales in the style of J.S. Bach. R.P. Lippmann. In: Moody, J.E., Touretzky, D.S. (Eds.), Advances in Neural Information Processing, Morgan Kaufmann, San Mateo, California. pp. 267-274.

[11]

Holst, I., ABC of Music. Oxford University Press, Oxford.

[12]

Hörnel, D. and Menzel, W., Learning musical style and style with neural networks. Computer Music Journal. v22.

[13]

Jeppesen, K., Counterpoint The Polyphonic Vocal Style of the Sixteenth Century. Dover Publications, Inc., New York.

[14]

R. Leighton, Aspirin & Migraines. Release V7.0 Users' Manual, 1994.

[15]

Mitchell, T., Machine Learning. McGraw-Hill International Editions.

[16]

Mozer, M.C., Connectionist music composition based on melodic, stylistic, and psychophysical constraints. Music and Connectionism. v3 i2. 195-211.

[17]

Rendel, D.M., The New Harvard Dictionary of Music. The Belknap Press of Harvard University Press, Cambridge, Massachusetts, London, England.

[18]

Russell, S. and Norvig, P., Artificial Intelligence - A Modern Approach. Prentice-Hall, Inc., Englewood.

Digital Library

[19]

Todd, P.M., A connectionist approach to algorithmic composition. Computer Music Journal. v22.

[20]

Widmer, G., A knowledge intensive approach to machine learning in music. In: Balaban, M., Ebcioglu, K., Laske, O. (Eds.), Understanding Music with AI: Perspectives On Music Cognition, AAAI Press, Menlo Park, CA.

Cited By

Abboud RTekli J(2020)Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and compositionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-04503-424:13(9875-9925)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1007/s00500-019-04503-4
Prudente LCoronel A(2017)Towards Automated Counter-Melody Generation for Monophonic MelodiesProceedings of the 2017 International Conference on Machine Learning and Soft Computing10.1145/3036290.3036295(197-202)Online publication date: 13-Jan-2017
https://dl.acm.org/doi/10.1145/3036290.3036295
Fernández JVico F(2013)AI methods in algorithmic compositionJournal of Artificial Intelligence Research10.5555/2591248.259126048:1(513-582)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.5555/2591248.2591260
Show More Cited By

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A machine learning approach to two-voice counterpoint composition

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Copyright © Elsevier B.V. © 2006.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 April 2007

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Cited By

Abboud RTekli J(2020)Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and compositionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-04503-424:13(9875-9925)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1007/s00500-019-04503-4
Prudente LCoronel A(2017)Towards Automated Counter-Melody Generation for Monophonic MelodiesProceedings of the 2017 International Conference on Machine Learning and Soft Computing10.1145/3036290.3036295(197-202)Online publication date: 13-Jan-2017
https://dl.acm.org/doi/10.1145/3036290.3036295
Fernández JVico F(2013)AI methods in algorithmic compositionJournal of Artificial Intelligence Research10.5555/2591248.259126048:1(513-582)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.5555/2591248.2591260
Herremans DSöRensen K(2013)Composing fifth species counterpoint music with a variable neighborhood search algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.05.07140:16(6427-6437)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1016/j.eswa.2013.05.071
Yilmaz ATelatar Z(2009)Potential applications of fuzzy logic in musicProceedings of the 18th international conference on Fuzzy Systems10.5555/1717561.1717679(670-675)Online publication date: 20-Aug-2009
https://dl.acm.org/doi/10.5555/1717561.1717679

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