TY - JOUR
T1 - Designing Percussive Timbre Remappings: Negotiating Audio Representations and Evolving Parameter Spaces
AU - Shier, Jordie
AU - Constanzo, Rodrigo
AU - Saitis, Charalampos
AU - Robertson, Andrew
AU - McPherson, Andrew
PY - 2025/6
Y1 - 2025/6
N2 - imbre remapping is an approach to audio-to-synthesizer parameter mapping that aims to transfer timbral expressions from a source instrument onto synthesizer controls. This process is complicated by the ill-defined nature of timbre and the complex relationship between synthesizer parameters and their sonic output. In this work, we focus on real-time timbre remapping with percussion instruments, combining technical development with practice-based methods to address these challenges. As a technical contribution, we introduce a genetic algorithm - applicable to black-box synthesizers including VSTs and modular synthesizers - to generate datasets of synthesizer presets that vary according to target timbres. Additionally, we propose a neural network-based approach to predict control features from short onset windows, enabling low-latency performance and feature-based control. Our technical development is grounded in musical practice, demonstrating how iterative and collaborative processes can yield insights into open-ended challenges in DMI design. Experiments on various audio representations uncover meaningful insights into timbre remapping by coupling data-driven design with practice-based reflection. This work is accompanied by an annotated portfolio, presenting a series of musical performances and experiments with reflections.
AB - imbre remapping is an approach to audio-to-synthesizer parameter mapping that aims to transfer timbral expressions from a source instrument onto synthesizer controls. This process is complicated by the ill-defined nature of timbre and the complex relationship between synthesizer parameters and their sonic output. In this work, we focus on real-time timbre remapping with percussion instruments, combining technical development with practice-based methods to address these challenges. As a technical contribution, we introduce a genetic algorithm - applicable to black-box synthesizers including VSTs and modular synthesizers - to generate datasets of synthesizer presets that vary according to target timbres. Additionally, we propose a neural network-based approach to predict control features from short onset windows, enabling low-latency performance and feature-based control. Our technical development is grounded in musical practice, demonstrating how iterative and collaborative processes can yield insights into open-ended challenges in DMI design. Experiments on various audio representations uncover meaningful insights into timbre remapping by coupling data-driven design with practice-based reflection. This work is accompanied by an annotated portfolio, presenting a series of musical performances and experiments with reflections.
U2 - 10.5281/zenodo.15698926
DO - 10.5281/zenodo.15698926
M3 - Conference article
SN - 2220-4792
SP - 452
EP - 461
JO - Proceedings of the International Conference on New Interfaces for Musical Expression
JF - Proceedings of the International Conference on New Interfaces for Musical Expression
M1 - 66
ER -