Abstract
The application of artificial intelligence in the musical field is bringing about a rapid and radical transformation, with extraordinary potential to change the way music is created, analyzed and interpreted. This article intends to make an innovative contribution in the definition of a computational model capable of modeling the musical expressiveness of a score considered at its symbolic level (musical notation). To achieve this goal, the study used functional harmony to segment the musical score and information theory for the definition of musical dynamics: the organization of the executive intensity of the written notes respecting the stylistic and functional aspects of the musical composition. The results are encouraging because on the one hand they are very close to the executive interpretation of humans and on the other hand they show that the proposed model is “idiom-independent”, therefore capable of functioning regardless of compositional styles, and can be useful in a variety of music applications such as adaptive automatic music performance and score analysis.
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
Westergaard, P., Hiller, L. A.: Review of experimental music. Composition with an electronic computer, by L. M. Isaacson. J. Music Theory, 3(2), 302–306 (1959). https://doi.org/10.2307/842857
Liu, T., Ramakrishnan, B.: Bach in 2014: Music Composition with Recurrent Neural Network (2014). https://arxiv.org/abs/1412.3191
Hewahi, N.M., AlSaigal, S., AlJanahi, S.: Generation of music pieces using machine learning: long short-term memory neural networks approach. Arab J. Basic Appl. Sci. 26, 397–413 (2019)
Zhou, J., Zhu, H., Wang, X.: Choir Transformer: Generating Polyphonic Music with Relative Attention on Transformer (2023). https://arxiv.org/abs/2308.02531
Floridi, L.: The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities. (Oxford, 2023; online edn, Oxford Academic) (2023). https://doi.org/10.1093/oso/9780198883098.001.0001
Carnovalini, F., Roddà, A.: Computational creativity and music generation systems: an introduction to the state of the art. Front. Artif. Intell. 3(14), 1–20 (2020)
Essl, K.: Algorithmic Composition. In: Collins, N., d’Escrivan, J. Cambridge Companion to Electronic Music. Cambridge University Press (2007)
Matic, D.: A genetic algorithm for composing music. Yugoslav J. Oper. Res. 20, 157–177 (2010)
Hui Yap, A.Y., Soong, H., Hong Tse, S.S.: Real-time evolutionary music composition using JFUGUE and genetic algorithm. In: 2021 IEEE 19th Student Conference on Research and Development (SCOReD), pp. 377–382 (2021)
Shukla, S., Banka, H.: Monophonic music composition using genetic algorithm and Bresenham’s line algorithm. Multimedia Tools Appl. 81, 26483–26503 (2022)
Gupta, S., Majumdar, R., Gambhir, S.: An approach to generate music using genetic algorithm. In: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp. 1–5 (2022). https://doi.org/10.1109/ICRITO56286.2022.9964579
Zulić, H.: How AI can change/improve/influence music composition, performance and education: three case studies. INSAM J. Contemp. Music, Art Technol. (2019)
Zhang, F., Meng, H., Li, M., Cui, R., Liu, C.: Music emotion recognition based on chord identification. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds.) ICNC-FSKD 2020, vol. 88, pp. 956–963. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70665-4_103
Zhou, X., Lerch, A.: Chord detection using deep learning. In: Proceedings of the 16th ISMIR Conference, vol. 53 (2015)
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.) ISDA 2022, vol. 646, pp. 217–229. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-27440-4_21
Xu, Z.: Construction of intelligent recognition and learning education platform of national music genre under deep learning. Front. Psychol. 13, 843427 (2022). https://doi.org/10.3389/fpsyg.2022.843427
Della Ventura, M.: Implementation of an automatic musical scores recognition system. In: Haber, P., Lampoltshammer, T., Mayr, M., Plankensteiner K. (eds.) Data Science – Analytics and Applications. Springer Vieweg, Wiesbaden (2021). https://doi.org/10.1007/978-3-658-32182-6_8
Nam, J., Choi, K., Lee, J., Chou, S.Y., Yang, Y.H., et al.: Deep learning for audio-based music classification and tagging: teaching computers to distinguish rock from bach. IEEE Signal Process. Mag. 20, 89–90 (2019). https://doi.org/10.1109/MSP.2018.2874383
Muneratti Ortega, F.J.: A machine learning approach to computer modeling of musicals expression for performance learning and practice (2021). https://www.tdx.cat/bitstream/handle/10803/674200/tfjmo.pdf?sequence=1&isAllowed=y
Zhang, P., et al.: A deep neural network for modeling music. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 379–386 (2015)
Marchini, M., Ramirez, R., Papiotis, P., Maestre, E.: The sense of ensemble: a machine learning approach to expressive performance modelling in string quartets. J. New Music Res. 43(3), 303–317 (2014). https://doi.org/10.1080/09298215.2014.922999
Ramirez, R., Hazan, A., Maestre, E., Serra, X.: A data mining approach to expressive music performance modeling. In: Petrushin, V.A., Khan, L. (eds.) Multimedia Data Mining and Knowledge Discovery. Springer, London (2007). https://doi.org/10.1007/978-1-84628-799-2_18
Arbo, A.: Entendre comme: Wittgenstein et l’esthétique musicale, Hermann, Paris 2013, pp. 224–232 (2013)
Barakat, H., Turk, O., Demiroglu, C.: Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources. J Audio Speech Music Proc 2024, 11 (2024). https://doi.org/10.1186/s13636-024-00329-7
de la Motte, D.: Harmonielehre. Bärenreiter (1976)
Della Ventura, M.: The influence of the rhythm with the pitch on melodic segmentation. In Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2015), pp. 191–201. Ostrava, Czech Republic, Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21206-7_17
Fubini, E.: La musica: natura e storia. Einaudi, Torino (2004)
Weaver, W., Shannon, C.: The Mathematical Theory of Information. Illinois Press, Urbana (1964)
Lerdhal, F., Jackendoff, R.: A Grammatical Parallel between Music and Language. Plenum Press, New York (1982)
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 Statistical Approach for Modeling the Expressiveness of Symbolic Musical Text. In: Younas, M., Awan, I., Petcu, D., Feng, B. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2024. Lecture Notes in Computer Science, vol 14792. Springer, Cham. https://doi.org/10.1007/978-3-031-68005-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-68005-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-68004-5
Online ISBN: 978-3-031-68005-2
eBook Packages: Computer ScienceComputer Science (R0)