CTAG-FX: Reinterpreting Synthesizer Parameter Spaces for Expressive Tone-Shaping Audio FX Design

2026-10-06

AI Summary

This study addresses the limitation of existing audio effects research, which predominantly focuses on parameter mapping while neglecting the construction of internal control spaces. We propose a novel design paradigm that transforms synthesizer parameter spaces into effect control spaces, restructuring 78 synthesizer parameters into timbre-shaping controls to enable reusable processing chain designs. Methodologically, we employ a retrieval-augmented A&R-CTAG model to generate text-conditioned configurations, validated through signal-level analysis. Experimental results demonstrate that the proposed role-based mapping approach yields significant spectral differences compared to random mapping baselines, effectively enhancing both the controllability and reusability of audio effects processors.

Abstract

Recent text-guided audio FX research has focused on translating natural-language descriptions into parameters or chain configurations within existing FX systems. However, comparatively little attention has been paid to how the internal control space of an individual FX processor can itself be constructed. To address this gap, we propose CTAG-FX, which functionally reinterprets the roles of 78 parameters in a text-conditioned synthesizer configuration as controls of a tone-shaping FX processor. Each synthesizer configuration defines a fixed tone-shaping processor that can be repeatedly applied to new audio inputs rather than producing only a one-off rendered output. The text-conditioned synthesizer configurations are generated using A&R-CTAG, a retrieval-enhanced extension of CTAG. We evaluate CTAG-FX through signal-level analysis, together with a scrambled-mapping ablation in which parameter-to-control assignments are randomly permuted to assess the contribution of the proposed role assignment. Role-based mapping produces prompt-distinct spectral and nonlinear behavior, whereas scrambling reduces this prompt-specific differentiation and yields a more prompt-insensitive nonlinear profile. Overall, these results suggest that synthesizer parameter spaces can serve not only as sound-generation spaces but also as design resources for constructing new audio-FX control spaces. Audio samples are available at https://taylor3527eg-hub.github.io/ctag-fx-platform-demo/.

Problem

• audio effects
• text-guided audio
• control space
• synthesizer parameters
• tone-shaping

Innovation

• Audio Effects Design
• Text-Conditioned Synthesizer
• Parameter Space Reinterpretation
• Retrieval-Augmented Generation
• Tone-Shaping