Miguel Molina-Solana

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.

@article{Molina2010a,
  title = {Identifying Violin Performers by their Expressive Trends},
  author = {Molina-Solana, Miguel and Arcos, Josep~Lluis and G\'omez, Emilia},
  journal = {Intelligent Data Analysis},
  year = {2010},
  number = {5},
  pages = {555--571},
  volume = {14},
  comment = {https://www.doc.ic.ac.uk/~mmolinas/publications/molina-ida10.pdf},
  doi = {10.3233/IDA-2010-0439},
  timestamp = {02}
}