  Analog audio effects and synthesizers often owe their distinct sound to
circuit nonlinearities. Faithfully modeling such significant aspect of the
original sound in virtual analog software can prove challenging. The current
work proposes a generic data-driven approach to virtual analog modeling and
applies it to the Fender Bassman 56F-A vacuum-tube amplifier. Specifically, a
feedforward variant of the WaveNet deep neural network is trained to carry out
a regression on audio waveform samples from input to output of a SPICE model of
the tube amplifier. The output signals are pre-emphasized to assist the model
at learning the high-frequency content. The results of a listening test suggest
that the proposed model accurately emulates the reference device. In
particular, the model responds to user control changes, and faithfully
restitutes the range of sonic characteristics found across the configurations
of the original device.
