Representation and Discovery of Vertical Patterns in Music

The automated discovery of recurrent patterns in music is a fundamental task in computational music analysis. This paper describes a new method for discovering patterns in the vertical and horizontal dimensions of polyphonic music. A formal representation of music objects is used to structure the musical surface, and several ideas for viewing pieces as successions of vertical structures are examined. A knowledge representation method is used to view pieces as sequences of relationships between music objects, and a pattern discovery algorithm is applied using this view of the Bach chorale harmonizations to find significant recurrent patterns. The method finds a small set of vertical patterns that occur in a large number of pieces in the corpus. Most of these patterns represent specific voice leading formulae within cadences.

[1]  Camilo Rueda,et al.  Objects, Time and Constraints in OpenMusic , 1998, ICMC.

[2]  Darrell Conklin,et al.  Representation and Discovery of Multiple Viewpoint Patterns , 2001, ICMC.

[3]  Chris Mellish,et al.  Statistical Learning of Harmonic Movement , 1999 .

[4]  Robert L. Mercer,et al.  Class-Based n-gram Models of Natural Language , 1992, CL.

[5]  Ian H. Witten,et al.  Multiple viewpoint systems for music prediction , 1995 .

[6]  Jean-Gabriel Ganascia,et al.  Musical Pattern Extraction and Similarity Assessment , 2000, Readings in Music and Artificial Intelligence.

[7]  Paul Hudak,et al.  Haskore music notation – An algebra of music – , 1996, Journal of Functional Programming.

[8]  Jorma Tarhio,et al.  Searching monophonic patterns within polyphonic sources , 2000 .

[9]  David Lewin,et al.  Re: Intervallic Relations between Two Collections of Notes , 1959 .

[10]  David Lewin,et al.  Generalized Musical Intervals and Transformations , 1987 .

[11]  Alan Smaill,et al.  Hierarchical music representation for composition and analysis , 1993, Comput. Humanit..

[12]  Mira Balaban,et al.  The Music Structures Approach to Knowledge Representation for Music Processing , 1996 .

[13]  David Cope,et al.  Computers and Musical Style , 1993 .

[14]  Dominik Hörnel,et al.  Investigating the Influence of Representations and Algorithms in Music Classification , 2001, Comput. Humanit..

[15]  Arbee L. P. Chen,et al.  Discovering nontrivial repeating patterns in music data , 2001, IEEE Trans. Multim..

[16]  D. Huron.  What is a Musical Feature? Forte’s Analysis of Brahms’s Opus 51, No. 1, Revisited , 2001 .

[17]  Dan Gusfield,et al.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[18]  Dan Gusfield,et al.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[19]  Bernd Schöner,et al.  Analysis and Synthesis of Palestrina-Style Counterpoint Using Markov Chains , 2001, ICMC.

[20]  Emilios Cambouropoulos,et al.  Towards a General Computational Theory of Musical Structure , 1998 .

[21]  Geraint A. Wiggins,et al.  Pattern Induction and matching in polyphonic music and other multidimensional datasets , 2001 .

[22]  Alan Smaill,et al.  Automatic Characterisation of Musical Style , 1993, Music Education: An Artificial Intelligence Approach.

[23]  William P. Birmingham,et al.  Automated Partitioning of Tonal Music , 2000, FLAIRS.

[24]  Alan Marsden,et al.  Representing Melodic Patterns as Networks of Elaborations , 2001, Comput. Humanit..

[25]  Nicholas Cook,et al.  A guide to musical analysis , 1987 .