  In music and audio production, attenuation of spectral resonances is an
important step towards a technically correct result. In this paper we present a
two-component system to automate the task of resonance equalization. The first
component is a dynamic equalizer that automatically detects resonances and
offers to attenuate them by a user-specified factor. The second component is a
deep neural network that predicts the optimal attenuation factor based on the
windowed audio. The network is trained and validated on empirical data gathered
from an experiment in which sound engineers choose their preferred attenuation
factors for a set of tracks. We test two distinct network architectures for the
predictive model and find that a dilated residual network operating directly on
the audio signal is on a par with a network architecture that requires a prior
audio feature extraction stage. Both architectures predict human-preferred
resonance attenuation factors significantly better than a baseline approach.
