  In this paper, we propose and develop the novel idea of treating musical
sheets as literary documents in the traditional text analytics parlance, to
fully benefit from the vast amount of research already existing in statistical
text mining and topic modelling. We specifically introduce the idea of
representing any given piece of music as a collection of "musical words" that
we codenamed "muselets", which are essentially musical words of various
lengths. Given the novelty and therefore the extremely difficulty of properly
forming a complete version of a dictionary of muselets, the present paper
focuses on a simpler albeit naive version of the ultimate dictionary, which we
refer to as a Naive Dictionary because of the fact that all the words are of
the same length. We specifically herein construct a naive dictionary featuring
a corpus made up of African American, Chinese, Japanese and Arabic music, on
which we perform both topic modelling and pattern recognition. Although some of
the results based on the Naive Dictionary are reasonably good, we anticipate
phenomenal predictive performances once we get around to actually building a
full scale complete version of our intended dictionary of muselets.
