  This paper describes a data-driven approach to creating real-time neural
network models of guitar amplifiers, recreating the amplifiers' sonic response
to arbitrary inputs at the full range of controls present on the physical
device. While the focus on the paper is on the data collection pipeline, we
demonstrate the effectiveness of this conditioned black-box approach by
training an LSTM model to the task, and comparing its performance to an offline
white-box SPICE circuit simulation. Our listening test results demonstrate that
the neural amplifier modeling approach can match the subjective performance of
a high-quality SPICE model, all while using an automated, non-intrusive data
collection process, and an end-to-end trainable, real-time feasible neural
network model.
