Abstract
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.
| Original language | English |
|---|---|
| Article number | 66 |
| Pages (from-to) | 452-461 |
| Number of pages | 10 |
| Journal | Proceedings of the International Conference on New Interfaces for Musical Expression |
| DOIs | |
| Publication status | Published - Jun 2025 |
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