Words in a natural language not only transmit information but also evolve
with the development of civilization and human migration. The same is true for
music. To understand the complex structure behind the music, we introduced an
algorithm called the Essential Element Network (EEN) to encode the audio into
text. The network is obtained by calculating the correlations between scales,
time, and volume. Optimizing EEN to generate Zipfs law for the frequency and
rank of the clustering coefficient enables us to generate and regard the
semantic relationships as words. We map these encoded words into the
scale-temporal space, which helps us organize systematically the syntax in the
deep structure of music. Our algorithm provides precise descriptions of the
complex network behind the music, as opposed to the black-box nature of other
deep learning approaches. As a result, the experience and properties
accumulated through these processes can offer not only a new approach to the
applications of Natural Language Processing (NLP) but also an easier and more
objective way to analyze the evolution and development of music.Comment: 5 page