Miguel Molina

Identifying performers by their expressive trends (27/07/10)

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, this approach performs a qualitative analysis of the detected trends for a given set of melodic patterns. The feasibility of the approach is shown for a dataset of monophonic violin recordings from 23 well-known performers. The presentation will also cover ongoing research on Music Performance, covering the issue of whether performances are shaped by the piece or the performer.

Department of Computing, Goldsmiths College, University of London, New Cross, London, SE14 6NW

Tel: +44 (0) 20 7919 7850 | Fax: +44 (0) 20 7919 7853 | Email: computing@gold.ac.uk