An approach to predicting bowing control parameter contours in violin performance
We present a machine learning approach to modeling bowing control parameter/ncontours in violin performance. Using accurate sensing techniques/nwe obtain relevant timbre-related bowing control parameters such as bow/ntransversal velocity, bow pressing force, and bow-bridge distance of each/nperforme...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/12399 |
| Acceso en línea: | http://hdl.handle.net/10230/12399 http://dx.doi.org/10.3233/IDA-2010-0441 |
| Access Level: | acceso abierto |
| Palabra clave: | Violí, Música per a So -- Tractament per ordinador |
| Sumario: | We present a machine learning approach to modeling bowing control parameter/ncontours in violin performance. Using accurate sensing techniques/nwe obtain relevant timbre-related bowing control parameters such as bow/ntransversal velocity, bow pressing force, and bow-bridge distance of each/nperformed note. Each performed note is represented by a curve parameter/nvector and a number of note classes are defined. The principal components/nof the data represented by the set of curve parameter vectors are obtained/nfor each class. Once curve parameter vectors are expressed in the new space/ndefined by the principal components, we train a model based on inductive/nlogic programming, able to predict curve parameter vectors used for rendering/nbowing controls. We evaluate the prediction results and show the potential/nof the model by predicting bowing control parameter contours from an/nannotated input score. |
|---|