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/.
Figures & tables
Figure 1: Representative prior text-guided FX workflow.
Dimension
Prior text-guided FX systems
CTAG-FX
Strategy
Text → predefined FX configuration
Text → synth representation → FX processor design
Source of expressivity
Available FX modules and chains
Synthesizer parameter space
Design space
Predefined FX parameter or chain space
Reinterpreted synth-to-FX control space
Main mechanism
Predict / optimize / search / refine
Reinterpret / map / design
Design object
FX parameter or FX chain
Self-contained tone-shaping FX processor
Contribution
Semantic control within predefined FX spaces
Synth-derived FX control-space design
Table 1: Conceptual comparison of prior text-guided FX systems and CTAG-FX.
Figure 2: Overall CTAG-FX workflow. A&R-CTAG generates a text-conditioned synthesizer configuration, and CTAG-FX maps the functional roles of its parameter groups to controls in a tone-shaping processor for external audio.
Figure 3: A&R-CTAG pipeline for generating the synth-side character representation used by CTAG-FX.
Figure 4: Temporal and spectral characteristics of a plucked E4 guitar note motivating the ADSR-to-EQ reinterpretation: (a) normalized RMS envelope with temporal markers, (b) spectrogram with harmonic guides.
Figure 5: Conceptual VCO-to-drive reinterpretation. (a) Sine and SquareSaw VCOs generate comparatively smooth and harmonically rich sources, respectively. (b) Drive 1 and Drive 2/Fuzz apply analogous smooth and aggressive nonlinear coloration to a common input. Colors indicate corresponding roles; waveforms are illustrative, not equivalent.
Figure 6: Simplified CTAG-FX signal flow with synth-derived control routing. Parentheses identify the corresponding synthesizer groups.
Synth Group
Functional Reinterpretation
FX Control
MIDI f0
Pitch reference → input-response reference
Input Trim
Duration
Event length → output-scale reference
Output Trim
ADSR1
Temporal envelope → spectral contour
Pre-EQ
ADSR2
Secondary envelope → complementary tone contour
Tone Stack
VCO1/2
Waveform source → nonlinear harmonic character
Drive 1/2
Mixer
Source balance → processing-path balance
Clean / Processed / Noise Mixer
Table 2: Summary of synthesizer groups, functional reinterpretations, and FX controls in CTAG-FX.
Figure 7: Spectral characterization and mapping ablation for four tone prompts. (a) Mean output-to-input spectral difference across ten configurations. (b) Role-based versus scrambled-mapping responses. Shading indicates 95% confidence intervals.
Figure 8: Nonlinear descriptors under role-based and scrambled mappings. Markers and error bars show configuration-level means and 95% confidence intervals ( N=10 per prompt).
Metric
CTAG
A&R-CTAG
Change
Initial score mean
0.1449
0.2981
+105.7%
Final score mean
0.6166
0.7545
+22.4%
Final score SD
0.1246
0.0595
−52.2%
Score change mean
0.4718
0.4564
−3.3%
Table 3: LAION-CLAP similarity results for CTAG and A&R-CTAG across ten prompts under a 300-iteration budget. Mean scores and final-score SD are computed across prompts; Change is relative to CTAG.