  If a cultural feature is transmitted over generations and exposed to
stochastic selection when spreading in a population, its evolution may be
governed by statistical laws and be partly predictable, as in the case of
genetic evolution. Music exhibits steady changes of styles over time, with new
characteristics developing from traditions. Recent studies have found trends in
the evolution of music styles, but little is known about their relations to the
evolution theory. Here we analyze Western classical music data and find
statistical evolutionary laws. For example, distributions of the frequencies of
some rare musical events (e.g. dissonant intervals) exhibit steady increase in
the mean and standard deviation as well as constancy of their ratio. We then
study an evolutionary model where creators learn their data-generation models
from past data and generate new data that will be socially selected by
evaluators according to the content dissimilarity (novelty) and style
conformity (typicality) with respect to the past data. The model reproduces the
observed statistical laws and can make non-trivial predictions for the
evolution of independent musical features. In addition, the same model with
different parameterization can predict the evolution of Japanese enka music,
which is developed in a different society and has a qualitatively different
tendency of evolution. Our results suggest that the evolution of musical styles
can partly be explained and predicted by the evolutionary model incorporating
statistical learning, which can be important for other cultures and future
music technologies.
