Virtual Analog

Virtual analog (VA) modeling aims to replicate the behavior of analog electronic circuits, particularly in audio processing, using computational methods. Current research heavily utilizes neural networks, including recurrent neural networks (RNNs), state-space models, and neural ordinary differential equations (NODEs), focusing on improving accuracy, efficiency, and parameter conditioning for various audio effects like distortion and equalization. These advancements are driven by the need for more realistic and computationally efficient simulations of analog systems, impacting fields such as music technology, signal processing, and potentially even analog computing hardware itself. The development of novel training algorithms, such as those leveraging analog computing hardware, further enhances the speed and efficiency of these models.