  In recent years, machine learning has been widely adopted to automate the
audio mixing process. Automatic mixing systems have been applied to various
audio effects such as gain-adjustment, equalization, and reverberation. These
systems can be controlled through visual interfaces, providing audio examples,
using knobs, and semantic descriptors. Using semantic descriptors or textual
information to control these systems is an effective way for artists to
communicate their creative goals. In this paper, we explore the novel idea of
using word embeddings to represent semantic descriptors. Word embeddings are
generally obtained by training neural networks on large corpora of written
text. These embeddings serve as the input layer of the neural network to create
a translation from words to EQ settings. Using this technique, the machine
learning model can also generate EQ settings for semantic descriptors that it
has not seen before. We compare the EQ settings of humans with the predictions
of the neural network to evaluate the quality of predictions. The results
showed that the embedding layer enables the neural network to understand
semantic descriptors. We observed that the models with embedding layers perform
better than those without embedding layers, but still not as good as human
labels.
