Deep Learning for Tube Amplifier Emulation
This work addresses the challenge of creating realistic virtual analog software for audio engineers and musicians, though it is incremental as it applies an existing deep learning method to a specific domain.
The authors tackled the problem of modeling analog audio effects by proposing a data-driven approach using a feedforward WaveNet to emulate a Fender Bassman tube amplifier, achieving accurate emulation as confirmed by listening tests.
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.