Abstract
The interest of musicians and computer scientists in AI-based automatic melody harmonization has increased significantly in the last few years. This research area has attracted the attention of both teachers and students of Theory, Analysis and Composition, looking for support tools for the learning process. The main problem is that the systems designed and developed up to now harmonize a melody written by a user without considering the didactic and therefore cognitive aspects at the basis of a “significant learning”: given a melody, the system returns a harmonization finished without any user input. This paper describes a self-learning algorithm capable of harmonizing a musical melody, with the aim of supporting the student during the study of Theory, Analysis and Composition. The algorithm, on the basis of the ascending and descending movement of the sounds of the melody (soprano), proposes the sounds for the bass line: the Viterbi algorithm was applied to evaluate the probability of the best match between the melody sounds and the provided Markov chains, to reach the “optimal” state sequences. Subsequently, the algorithm allows the user to complete the chords for each sound of the bass line (tenor and alto), or to create the complete chords. Examples of musical fragments harmonized in this way demonstrate that the algorithm is able to respect the concatenation rules of the tonal functions which characterize classical tonal music.
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
References
Paiement, J.F., Eck, D., Bengio, S.: Probabilistic melodic harmonization. In Conference of the Canadian Society for Computational Studies of Intelligence, pp. 218–229 (2006)
Tsushima, H., Nakamura, E., Itoyama, K., Yoshii, K.: Function- and rhythm-aware melody harmonization based on tree-structured parsing and split-merge sampling of chord sequences. In: Proceedings of the International Society for Music Information Retrieval Conference (2017)
Ventura, M.D.: A self-adaptive learning music composition algorithm as virtual tutor. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds.) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP AICT, vol. 646, pp. 16–26. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08333-4_2
Briot, J.P., Hadjeres, G., Pachet, F.D.: Deep learning techniques for music generation: a survey (2017). http://arxiv.org/abs/1709.01620
Lim, H., Rhyu, S., Lee, K.: Chord generation from symbolic melody using BLSTM networks. In: Proceedings of the ISMIR 2017 (2017)
Boenn, G., Brain, M., De Vos, M., Ffitch, J.: ANTON: a rule based composition system. In: Proceedings of the International Computer Music Conference 2011, University of Huddersfield (2011)
Everardo, F., Aguilera, A.: Armin: automatic trance music composition using answer set programming. Fundam. Inform. 113(1), 79–96 (2011)
Kippen, J., Bel. B.: Computers, composition, and the challenge of “new music” in modern India. Leonardo Music Journal, Massachusetts Institute of Technology Press (MIT Press), 1994, vol. 4, pp.79–84 (1994)
Bartoli, G.: Psicologia della creatività. Le condotte artistiche e scientifiche. Roma, Monolite, 2005, p. 19 (2005)
Ahmed, A.A., Ganapathy, A.: Creation of automated content with embedded artificial intelligence: a study on learning management system for educational entrepreneurship. Acad. Entrep. J. 27(3), 1–10 (2021)
Ausbel D.P.: Educazione e processi cognitivi. Ed. Franco Angeli, Milano 2004 (2004)
Novak, J.: L’apprendimento significativo. Ed. Erickson, Trento 2001 (2001)
Adorno, T.W.: Kierkegaard. La costruzione dell’estetico, Longanesi, Milano 1983 (1983)
Coltro, B.: Lezioni di armonia complementare. Ed. Zanibon (1997)
Schoenberg, A.: Theory and Harmony. Univ of California Pr; Reprint edition (1992)
de la Motte, D.: Manuale di armonia, Bärenreiter (1976)
Novak, J.: L’apprendimento significativo. Erickson, Portland (2001)
Bandura, A.: Social Foundations of Thought and Action. Prentice-Hall, Englewood Cliffs, NJ (1986)
Bandura, A.: Self-Efficacy: The Exercise of Control. W.H. Freeman, New York (1997)
Ames, C.: The Markov process as a compositional model: a survey and tutorial. Leonardo 1989(22), 175–187 (1989)
Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 1967(13), 260–269 (1967)
Forney, G.: The viterbi algorithm. Proc. IEEE 1973(61), 268–278 (1973)
Bockmayr and Reinert (2011). Markov chains and Hidden Markov Models. Discrete Math for Bioinformatics WS 10/11
Della Ventura, M.: Human-centred artificial intelligence in sound perception and music composition. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds.) Intelligent Systems Design and Applications. ISDA 2022. LNNS, vol. 646, pp. 217–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-27440-4_21
Flavell, J.H.: First discussant’s comments. What is memory development the development of? Hum. Dev. 14, 272–278 (1971)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ventura, M.D. (2024). A Deep Learning Algorithm for the Development of Meaningful Learning in the Harmonization of a Musical Melody. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham. https://doi.org/10.1007/978-3-031-48981-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-48981-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48980-8
Online ISBN: 978-3-031-48981-5
eBook Packages: Computer ScienceComputer Science (R0)