Affiliations: [a] Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada, Spain | [b] Artificial Intelligence Research Institute (IIIA), Spanish National Research Council (CSIC), Bellaterra, Spain | [c] Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain | [x] City University London, London, UK | [y] University of Athens, Athens, Greece | [z] Universitat Pompeu Fabra, Baralona, Spain
Correspondence:
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Corresponding author: Miguel Molina-Solana, Department of Computer Science and AI, ETSIIT, c/. Daniel Saucedo Aranda, s/n 18071 Granada, Spain. Tel.: +34 958 240806; Fax: +34 958 243317; E-mail: miguelmolina@ugr.es.
Abstract: Understanding the way performers use expressive resources of a given instrument to communicate with the audience is a challenging problem in the sound and music computing field. Working directly with commercial recordings is a good opportunity for tackling this implicit knowledge and studying well-known performers. The huge amount of information to be analyzed suggests the use of automatic techniques, which have to deal with imprecise analysis and manage the information in a broader perspective. This work presents a new approach, Trend-based modeling, for identifying professional performers in commercial recordings. Concretely, starting from automatically extracted descriptors provided by state-of-the-art tools, our approach performs a qualitative analysis of the detected trends for a given set of melodic patterns. The feasibility of our approach is shown for a dataset of monophonic violin recordings from 23 well-known performers.
