Nowadays we are often faced with huge databases resulting from the rapid
growth of data storage technologies. This is particularly true when dealing
with music databases. In this context, it is essential to have techniques and
tools able to discriminate properties from these massive sets. In this work, we
report on a statistical analysis of more than ten thousand songs aiming to
obtain a complexity hierarchy. Our approach is based on the estimation of the
permutation entropy combined with an intensive complexity measure, building up
the complexity-entropy causality plane. The results obtained indicate that this
representation space is very promising to discriminate songs as well as to
allow a relative quantitative comparison among songs. Additionally, we believe
that the here-reported method may be applied in practical situations since it
is simple, robust and has a fast numerical implementation.Comment: Accepted for publication in Physica