  We show that, when music pieces are cast in the form of time series of pitch
variations, the concepts and tools of dynamical systems theory can be applied
to the analysis of {\it temporal dynamics} in music. (i) Phase space portraits
are constructed from the time series wherefrom the dimensionality is evaluated
as a measure of the {\pit global} dynamics of each piece. (ii) Spectral
analysis of the time series yields power spectra ($\sim f^{-\nu}$) close to
{\pit red noise} ($\nu \sim 2$) in the low frequency range. (iii) We define an
information entropy which provides a measure of the {\pit local} dynamics in
the musical piece; the entropy can be interpreted as an evaluation of the
degree of {\it complexity} in the music, but there is no evidence of an
analytical relation between local and global dynamics. These findings are based
on computations performed on eighty sequences sampled in the music literature
from the 18th to the 20th century.
