  This work investigates alternate pre-emphasis filters used as part of the
loss function during neural network training for nonlinear audio processing. In
our previous work, the error-to-signal ratio loss function was used during
network training, with a first-order highpass pre-emphasis filter applied to
both the target signal and neural network output. This work considers more
perceptually relevant pre-emphasis filters, which include lowpass filtering at
high frequencies. We conducted listening tests to determine whether they offer
an improvement to the quality of a neural network model of a guitar tube
amplifier. Listening test results indicate that the use of an A-weighting
pre-emphasis filter offers the best improvement among the tested filters. The
proposed perceptual loss function improves the sound quality of neural network
models in audio processing without affecting the computational cost.
