Augmenting Parametric Synthesis with Learned Timbral Controllers
Jeff Gregorio, and Youngmoo Kim
Proceedings of the International Conference on New Interfaces for Musical Expression
- Year: 2019
- Location: Porto Alegre, Brazil
- Pages: 431–436
- DOI: 10.5281/zenodo.3673025 (Link to paper)
- PDF link
Abstract:
Feature-based synthesis applies machine learning and signal processing methods to the development of alternative interfaces for controlling parametric synthesis algorithms. One approach, geared toward real-time control, uses low dimensional gestural controllers and learned mappings from control spaces to parameter spaces, making use of an intermediate latent timbre distribution, such that the control space affords a spatially-intuitive arrangement of sonic possibilities. Whereas many existing systems present alternatives to the traditional parametric interfaces, the proposed system explores ways in which feature-based synthesis can augment one-to-one parameter control, made possible by fully invertible mappings between control and parameter spaces.
Citation:
Jeff Gregorio, and Youngmoo Kim. 2019. Augmenting Parametric Synthesis with Learned Timbral Controllers. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.3673025BibTeX Entry:
@inproceedings{Gregorio2019, abstract = {Feature-based synthesis applies machine learning and signal processing methods to the development of alternative interfaces for controlling parametric synthesis algorithms. One approach, geared toward real-time control, uses low dimensional gestural controllers and learned mappings from control spaces to parameter spaces, making use of an intermediate latent timbre distribution, such that the control space affords a spatially-intuitive arrangement of sonic possibilities. Whereas many existing systems present alternatives to the traditional parametric interfaces, the proposed system explores ways in which feature-based synthesis can augment one-to-one parameter control, made possible by fully invertible mappings between control and parameter spaces.}, address = {Porto Alegre, Brazil}, author = {Jeff Gregorio and Youngmoo Kim}, booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression}, doi = {10.5281/zenodo.3673025}, editor = {Marcelo Queiroz and Anna Xambó Sedó}, issn = {2220-4806}, month = {June}, pages = {431--436}, publisher = {UFRGS}, title = {Augmenting Parametric Synthesis with Learned Timbral Controllers}, url = {http://www.nime.org/proceedings/2019/nime2019_paper085.pdf}, year = {2019} }