Modeling violin performances using inductive logic programming

Issue title: Machine Learning and Music

Guest editors: Darrell Conklinx, Christina Anagnostopoulouy and Rafael Ramirezz

Article type: Research Article

Authors: Ramirez, Rafael^{a; *} | Perez, Alfonso^a | Kersten, Stefan^a | Rizo, David^b | Roman, Placido^b | Inesta, Jose M.^b

Affiliations: [a] Universitat Pompeu Fabra, Barcelona, Spain | [b] Universidad de Alicante, Alicante, Spain | [x] City University London, London, UK | [y] University of Athens, Athens, Greece | [z] Universitat Pompeu Fabra, Baralona, Spain

Correspondence: [*] Corresponding author: Rafael Ramirez, Universitat Pompeu Fabra, Ocata 1, 08003, Barcelona, Spain. Tel.: +34 935421365; Fax: +34 935422202; E-mail: [email protected].

Abstract: Professional musicians intuitively manipulate sound properties such as pitch, timing, amplitude and timbre in order to produce expressive performances of particular pieces. However, there is little explicit information about how and in which musical contexts this manipulation occurs. In this paper we describe a machine learning approach to modeling the knowledge applied by a musician when performing a score in order to produce an expressive performance. In particular, we apply inductive logic programming techniques in order to automatically learn models for both understanding and generating expressive violin performances.

DOI: 10.3233/IDA-2010-0440

Journal: Intelligent Data Analysis, vol. 14, no. 5, pp. 573-585, 2010