Interactive Neural Resonators
Rodrigo Diaz, Charalampos Saitis, and Mark B Sandler
Proceedings of the International Conference on New Interfaces for Musical Expression
- Year: 2023
- Location: Mexico City, Mexico
- Track: Work in Progress
- Pages: 569–573
- Article Number: 81
- DOI: 10.5281/zenodo.11189296 (Link to paper and supplementary files)
- PDF Link
Abstract
In this work, we propose a method for the controllable synthesis of real-time contact sounds using neural resonators. Previous works have used physically inspired statistical methods and physical modelling for object materials and excitation signals. Our method incorporates differentiable second-order resonators and estimates their coefficients using a neural network that is conditioned on physical parameters. This allows for interactive dynamic control and the generation of novel sounds in an intuitive manner. We demonstrate the practical implementation of our method and explore its potential creative applications.
Citation
Rodrigo Diaz, Charalampos Saitis, and Mark B Sandler. 2023. Interactive Neural Resonators. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.11189296 [PDF]
BibTeX Entry
@inproceedings{nime2023_81,
author = {Rodrigo Diaz and Charalampos Saitis and Mark B Sandler},
title = {Interactive Neural Resonators},
pages = {569--573},
numpages = {5},
booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
editor = {Miguel Ortiz and Adnan Marquez-Borbon},
year = {2023},
month = {May},
address = {Mexico City, Mexico},
issn = {2220-4806},
articleno = {81},
track = {Work in Progress},
doi = {10.5281/zenodo.11189296},
url = {https://nime.org/proceedings/2023/nime2023_81.pdf},
abstract = {In this work, we propose a method for the controllable synthesis of real-time contact sounds using neural resonators. Previous works have used physically inspired statistical methods and physical modelling for object materials and excitation signals. Our method incorporates differentiable second-order resonators and estimates their coefficients using a neural network that is conditioned on physical parameters. This allows for interactive dynamic control and the generation of novel sounds in an intuitive manner. We demonstrate the practical implementation of our method and explore its potential creative applications.}
}