Probabilistic Models for Designing Motion and Sound Relationships
Jules Françoise, Norbert Schnell, Riccardo Borghesi, and Frédéric Bevilacqua
Proceedings of the International Conference on New Interfaces for Musical Expression
- Year: 2014
- Location: London, United Kingdom
- Pages: 287–292
- DOI: 10.5281/zenodo.1178764 (Link to paper and supplementary files)
- PDF Link
Abstract
We present a set of probabilistic models that support the design of movement and sound relationships in interactive sonic systems. We focus on a mapping--by--demonstration approach in which the relationships between motion and sound are defined by a machine learning model that learns from a set of user examples. We describe four probabilistic models with complementary characteristics in terms of multimodality and temporality. We illustrate the practical use of each of the four models with a prototype application for sound control built using our Max implementation.
Citation
Jules Françoise, Norbert Schnell, Riccardo Borghesi, and Frédéric Bevilacqua. 2014. Probabilistic Models for Designing Motion and Sound Relationships. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.1178764 [PDF]
BibTeX Entry
@inproceedings{nime2014_jfrancoise1,
author = {Jules Françoise and Norbert Schnell and Riccardo Borghesi and Frédéric Bevilacqua},
title = {Probabilistic Models for Designing Motion and Sound Relationships},
pages = {287--292},
booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
year = {2014},
month = {June},
publisher = {Goldsmiths, University of London},
address = {London, United Kingdom},
issn = {2220-4806},
doi = {10.5281/zenodo.1178764},
url = {https://nime.org/proceedings/2014/nime2014_482.pdf},
abstract = {We present a set of probabilistic models that support the design of movement and sound relationships in interactive sonic systems. We focus on a mapping--by--demonstration approach in which the relationships between motion and sound are defined by a machine learning model that learns from a set of user examples. We describe four probabilistic models with complementary characteristics in terms of multimodality and temporality. We illustrate the practical use of each of the four models with a prototype application for sound control built using our Max implementation.}
}