Problem: Currently, TONEX captures static amp snapshots. Adjusting Gain or EQ after profiling relies on generic digital EQ/boost algorithms rather than reflecting the real non-linear behavior of tubes and circuitry in the profiled amplifier.
Proposed Solution (Guided Multi-Point Profiling): Introduce a new capture mode in the TONEX Modeler application that guides the user through taking 3–4 specific measurement points on the amp.
Step-by-step UI Prompts: The app explicitly instructs the user:
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Set Amp Gain to 3 → Capture (Low Gain)
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Set Amp Gain to 5 → Capture (Mid Gain)
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Set Amp Gain to 8 → Capture (High Gain)
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Set Amp Gain to 10 → Capture (Max Gain)
Algorithmic Interpolation: The AI neural network uses these baseline points to map the exact non-linear break-up, tube saturation, and tonal shift between positions.
Result: Turning the Gain knob inside TONEX accurately morphs between the captured physical behaviors of the amp, providing a 100% authentic reaction without making the user manually store separate profiles for every setting.
Key Features & Extended Functionality:
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Optional Deep EQ Mapping: Users can choose between a “Fast Capture” (focusing strictly on the 4 Gain interpolation points with EQ centered) or an “Advanced Capture” mode. The Advanced mode prompts for 2–3 additional snapshot points of the tone stack (e.g., extreme Bass/Treble responses), allowing the algorithm to accurately map the inter-dependent filtering behavior of the amp’s specific EQ circuit.
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Fully Customizable Sampling Resolution (User-Defined Points): Allow users to define how many measurement points they want to capture. A user can do a quick 3-point scan, or spend time capturing 10+ points across Gain and EQ sweeps. The AI scales its interpolation model based on the density of provided data, giving enthusiasts and professional profile creators total freedom to build the ultimate, single-file virtual replica of their amplifier.
Technical & System Considerations:
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Backward Compatibility: The existing single-snapshot capture process remains available as a “Legacy/Express Mode” for users who prefer an ultra-fast setup. Multi-point interpolation acts as an additive feature layer.
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Unified File Container (.tonex2 / Container Format): All interpolated measurement points and neural weight matrices should be packed into a single profile file. To the end-user, it appears as a single Tone Model with fully active, realistic knobs, keeping the ToneNET library clean and structured.
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Background Training Queue: Since training an interpolated AI model from multiple audio passes requires additional CPU/GPU processing, the TONEX Modeler app could feature a background processing queue (or cloud rendering option). Users can record all physical passes sequentially in a few minutes, and let the software process the AI model in the background while they continue playing.