  Structure is one of the most essential aspects of music, and music structure
is commonly indicated through repetition. However, the nature of repetition and
structure in music is still not well understood, especially in the context of
music generation, and much remains to be explored with Music Information
Retrieval (MIR) techniques. Analyses of two popular music datasets (Chinese and
American) illustrate important music construction principles: (1) structure
exists at multiple hierarchical levels, (2) songs use repetition and limited
vocabulary so that individual songs do not follow general statistics of song
collections, (3) structure interacts with rhythm, melody, harmony, and
predictability, and (4) over the course of a song, repetition is not random,
but follows a general trend as revealed by cross-entropy. These and other
findings offer challenges as well as opportunities for deep-learning music
generation and suggest new formal music criteria and evaluation methods. Music
from recent music generation systems is analyzed and compared to human-composed
music in our datasets, often revealing striking differences from a structural
perspective.
