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.