Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/IDA-2010-0439 | Intell. Data Anal. |
Keywords | Field | DocType |
expressive resource,automatic technique,imprecise analysis,expressive trend,broader perspective,well-known performer,identifying violin performer,challenging problem,commercial recording,new approach,qualitative analysis,trend-based modeling | Melody,Data set,Computer science,Implicit knowledge,Speech recognition,Violin,Artificial intelligence,Natural language processing,Machine learning | Journal |
Volume | Issue | ISSN |
14 | 5 | 1088-467X |
Citations | PageRank | References |
3 | 0.39 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miguel Molina-Solana | 1 | 48 | 12.80 |
| Josep Lluís Arcos | 2 | 1237 | 89.55 |
| Emilia Gómez | 3 | 139 | 29.70 |