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

Improved methods for pattern discovery in music, with applications in automated stylistic composition. Collins, T. E. Ph.D. Thesis, The Open University, August, 2011.

Paper abstract bibtex

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 Frededric 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 on the accompanying CD or at http://www.tomcollinsresearch.net

@PhDThesis{        collins2011-improved,
    author       = {Thomas Edward Collins},
    year         = {2011},
    title        = {Improved methods for pattern discovery in music, with
                   applications in automated stylistic composition},
    month        = {August},
    school       = {The Open University},
    url          = {https://oro.open.ac.uk/30103/},
    abstract     = {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 Frededric 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 on the
                   accompanying CD or at http://www.tomcollinsresearch.net}
}

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