Abstract
Expressiveness is an important aspect of a music composition. It becomes fundamental in an automatic music composition process, a domain where the Artificial Intelligent Systems have shown great potential and interest. The research presented in this paper describes an empirical approach to give expressiveness to a tonal melody generated by computers, considering both the symbolic music text and the relationships among the sounds of the musical text. The method adapts the musical expressive character to the musical text on the base of the “harmonic function” carried by every single musical chord. The article is intended to demonstrate the effectiveness of the method by applying it to some (tonal) musical pieces written in the 18th and 19th century. Future improvements of the method are discussed briefly at the end of the paper.
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Notes
- 1.
The interval between the various sounds is the distance separating a sound from another. The classification of an interval consists in the denomination (generic indication) and in the qualification (specific indication). The denomination corresponds to the number of degrees that the interval includes, calculated from the low one to the high one; it may be of a 2nd, a 3rd, 4th, 5th, and so on.; the qualification is deduced from the number of tones and semi-tones that the interval contains; it may be: perfect (G), major (M), minor (m), augmented (A), diminished (d), more than augmented (A+), more than diminished (d+), exceeding (E), deficient (df).
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Della Ventura, M. (2020). Shaping the Music Perception of an Automatic Music Composition: An Empirical Approach for Modelling Music Expressiveness. In: Madureira, A., Abraham, A., Gandhi, N., Silva, C., Antunes, M. (eds) Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). SoCPaR 2018. Advances in Intelligent Systems and Computing, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-17065-3_1
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