Abstract—This work investigates how opera singers manipulate timing in order to produce expressive performances that have common features but also bear a distinguishable personal style. We characterize performances not only relative to the score, but also consider the contribution of features extracted from the libretto. Our approach is based on applying machine learning to extract singer-specific patterns of expressive singing from performances by Josep Carreras and Placido Domingo. We compare and contrast some of these rules, and we draw some analogies between them and some of the general expressive performance rules existing in the literature.
Index Terms—Expressive performance, machine learning, timing model
M. Cristina Marinescu is with Universidad Carlos III de Madrid, Department of Computer Science, and Leganes 28911, Spain (e-mail: mcristina@arcos.inf.uc3m.es).
R. Ramirez is with Universitat Pompeu Fabra, Music Technology Group, Barcelona 08018, Spain (e-mail: rramirez@iua.upf.edu).
Cite: Maria Cristina Marinescu and Rafael Ramirez, "Learning Singer-Specific Performance Rules," International Journal of Modeling and Optimization vol. 2, no. 2, pp. 97-102, 2012.
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