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.3673025
BibTeX 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} }