Extensive studies have been conducted on both musical scores and audio tracks of western classical music with the finality of learning and detecting the key in which a particular piece of music was played. Both the Bayesian Approach and modern unsupervised learning via latent Dirichlet allocation have been used for such learning tasks. In this research work, 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 modeling.

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 supervised and unsupervised learning.

For the exploration of pattern recognition and topic modeling, we venture out of the traditional western classical music and embrace and explore other music genres. We consider the musical score sheets and audio tracks of some of the giants of jazz like Duke Ellington, Miles Davis, John Coltrane, Dizzie Gillespie, Wes Montgomery, Charlie Parker, Sonny Rollins, Louis Armstrong, Bill Evans, Dave Brubeck, Thelonious Monk. We specifically employ Bayesian techniques and modern topic modeling methods to explore tasks such as: automatic improvisation detection, genre identification, and key detection. Although some of the results based on the Naive Dictionary are reasonably good, we anticipate phenomenal predictive performances once we get around to actually build a full scale complete version of our intended dictionary of muselets.

Library of Congress Subject Headings

Music--Data processing; Data mining; Pattern perception; Text processing (Computer science)

Publication Date


Document Type


Student Type


Degree Name

Applied Statistics (MS)

Department, Program, or Center

School of Mathematical Sciences (COS)


Ernest Fokoue

Advisor/Committee Member

Joseph Voelkel

Advisor/Committee Member

Robert Parody


RIT – Main Campus

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