Improved methods for pattern discovery in music, with applications in automated stylistic composition

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Computational methods for intra-opus pattern discovery (discovering repeated
patterns within a piece of music) and stylistic composition (composing
in the style of another composer or period) can offer insights into how human
listeners and composers undertake such activities.

Two studies are reported  that demonstrate improved computational methods for pattern discovery in music. In the first, regression models are built with the aim of predicting
subjective assessments of a pattern’s salience, based on various quantifiable
attributes of that pattern, such as the number of notes it contains. Using
variable selection and cross-validation, a formula is derived for rating the
importance of a discovered pattern. In the second study, a music analyst
undertook intra-opus pattern discovery for works by Domenico Scarlatti and
Johann Sebastian Bach, forming a benchmark of target patterns. The performance
of two existing algorithms and one of my own creation, called SIACT
(Structure Induction Algorithm with Compactness Trawling), is evaluated
by comparison with this benchmark. SIACT out-performs the existing algorithms
with regard to recall and, more often than not, precision.

A third experiment is reported concerning human judgements of music excerpts that
are, to varying degrees, in the style of mazurkas by Fr´ed´edric Chopin. This
acts as an evaluation for two computational models of musical style, called
Racchman-Oct2010 and Racchmaninof-Oct2010 (standing for RAndom Constrained
CHain of MArkovian Nodes with INheritance Of Form), which are
developed over two chapters. The latter of these models applies SIACT and
the formula for rating pattern importance, using temporal and registral positions
of discovered patterns from an existing template piece to guide the
generation of a new passage of music. The precision and runtime of pattern
discovery algorithms, and their use for audio summarisation are among topics
for future work. Data and code related to this thesis is available

(Courtesy of Collins, Tom (2011). Improved methods for pattern discovery in music, with applications in automated stylistic composition. PhD thesis The Open University.)

 

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