At the core of our amplifier modeling technology lies a Volterra series framework, enhanced with advanced machine learning and neural architectures (NAM-inspired). Unlike traditional static models or simple convolution techniques, our approach captures not only the linear dynamics of an amplifier but also the nonlinear memory effects that define the true response of analog gear under real-world playing conditions.
The Volterra model is the gold standard for describing nonlinear dynamic systems such as guitar amplifiers. By expanding the signal into multidimensional kernels, we can precisely capture:
This goes beyond static snapshots or simple impulse responses—our solution preserves the full, living response of the hardware across a wide range of playing styles.
To push the Volterra framework further, we integrate deep learning architectures optimized for audio modeling. Neural networks act as adaptive kernel approximators, enabling:
Our models aren’t just trained to measure signals—they’re refined to preserve the musicality of the amplifier. Through a careful balance of physics-inspired Volterra kernels and AI-driven optimization, the resulting VST plugin provides:
We believe in transparent innovation—our expertise in Volterra and neural modeling is not just research, but technology you can play, record, and produce with today.
That’s why we are releasing a DEMO version of our VST, so musicians and producers can experience the difference firsthand. Follow the links: