  We propose a novel approach for the automatic equalization of individual
musical instrument tracks. Our method begins by identifying the instrument
present within a source recording in order to choose its corresponding ideal
spectrum as a target. Next, the spectral difference between the recording and
the target is calculated, and accordingly, an equalizer matching model is used
to predict settings for a parametric equalizer. To this end, we build upon a
differentiable parametric equalizer matching neural network, demonstrating
improvements relative to previously established state-of-the-art. Unlike past
approaches, we show how our system naturally allows real-world audio data to be
leveraged during the training of our matching model, effectively generating
suitably produced training targets in an automated manner mirroring conditions
at inference time. Consequently, we illustrate how fine-tuning our matching
model on such examples considerably improves parametric equalizer matching
performance in real-world scenarios, decreasing mean absolute error by 24%
relative to methods relying solely on random parameter sampling techniques as a
self-supervised learning strategy. We perform listening tests, and demonstrate
that our proposed automatic equalization solution subjectively enhances the
tonal characteristics for recordings of common instrument types.
