Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora

Did Ludwig van Beethoven (1770–1827) re-use material when composing his piano sonatas? What repeated patterns are distinctive of Beethoven’s piano sonatas compared, say, to those of Frederic Chopin (1810–1849)? Traditionally, in preparation for essays on topics such as these, music analysts have undertaken inter-opus pattern discovery—informally or systematically—which is the task of identifying two or more related note collections (or phenomena derived from those collections, such as chord sequences) that occur in at least two different movements or pieces of music. More recently, computational methods have emerged for tackling the inter-opus pattern discovery task, but often they make simplifying and problematic assumptions about the nature of music. Thus a gulf exists between the flexibility music analysts employ when considering two note collections to be related, and what algorithmic methods can achieve. By unifying contributions from the two main approaches to computational pattern discovery—viewpoints and the geometric method—via the technique of symbolic fingerprinting, the current chapter seeks to reduce this gulf. Results from six experiments are summarized that investigate questions related to borrowing, resemblance, and distinctiveness across 21 Beethoven piano sonata movements. Among these results, we found 2–3 bars of material that occurred across two sonatas, an andante theme that appears varied in an imitative minuet, patterns with leaps that are distinctive of Beethoven compared to Chopin, and two potentially new examples of what Meyer and Gjerdingen call schemata. The chapter does not solve the problem of inter-opus pattern discovery, but it can act as a platform for research that will further reduce the gap between what music informaticians do, and what musicologists find interesting.

[1]  Robin C. Laney,et al.  Modeling pattern importance in Chopin's mazurkas , 2011 .

[2]  Robert O. Gjerdingen,et al.  Music in the galant style , 2007 .

[3]  David Meredith,et al.  The ps13 pitch spelling algorithm , 2006 .

[4]  Mathieu Bergeron,et al.  Feature Set Patterns in Music , 2008, Computer Music Journal.

[5]  W. B. Haas,et al.  Discovering repeated patterns in music : state of knowledge , challenges , perspectives , 2013 .

[6]  Tom Collins,et al.  Stravinsqi/De Montfort University at the MediaEval 2014 C@merata Task , 2014, MediaEval.

[7]  Robert O. Gjerdingen,et al.  A Classic Turn of Phrase: Music and the Psychology of Convention , 1988 .

[8]  Emilios Cambouropoulos,et al.  Musical Parallelism and Melodic Segmentation: : A Computational Approach , 2006 .

[9]  John T. Winemiller Recontextualizing Handel's Borrowing , 1997 .

[10]  Leilani Kathryn Lutes Beethoven's re-uses of his own compositions, 1782-1826 , 1976 .

[11]  W. Bas de Haas,et al.  Finding Repeated Patterns in Music: State of Knowledge, Challenges, Perspectives , 2013, CMMR.

[12]  Gerhard Widmer,et al.  Fast Identification of Piece and Score Position via Symbolic Fingerprinting , 2012, ISMIR.

[13]  Gerhard Widmer,et al.  SIARCT-CFP: Improving Precision and the Discovery of Inexact Musical Patterns in Point-Set Representations , 2013, ISMIR.

[14]  Olivier Lartillot,et al.  Multi-Dimensional motivic pattern extraction founded on adaptive redundancy filtering , 2005 .

[15]  J. Forth Cognitively-motivated geometric methods of pattern discovery and models of similarity in music , 2012 .

[16]  H. Barlow,et al.  A dictionary of musical themes , 1975 .

[17]  E. C. Pielou,et al.  A TEST TO COMPARE THE INCIDENCE OF DISEASE IN ISOLATED AND CROWDED TREES , 1962 .

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

[19]  Philip Radcliffe,et al.  Beethoven's string quartets , 1965 .

[20]  Mathieu Bergeron,et al.  Discovery of Contrapuntal Patterns , 2010, ISMIR.

[21]  Nicholas Marston,et al.  Beethoven's piano sonata in E, op. 109 , 1995 .

[22]  William Earl Caplin,et al.  Analyzing Classical Form: An Approach for the Classroom , 2013 .

[23]  Geraint A. Wiggins,et al.  Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music , 2002 .

[24]  Darrell Conklin,et al.  Discovery of distinctive patterns in music , 2010, Intell. Data Anal..

[25]  Robin C. Laney,et al.  A Comparative Evaluation of Algorithms for Discovering Translational Patterns in Baroque Keyboard Works , 2010, ISMIR.

[26]  Karl Pearson,et al.  ON A NEW METHOD OF DETERMINING CORRELATION BETWEEN A MEASURED CHARACTER A, AND A CHARACTER B, OF WHICH ONLY THE PERCENTAGE OF CASES WHEREIN B EXCEEDS (OR FALLS SHORT OF) A GIVEN INTENSITY IS RECORDED FOR EACH GRADE OF A , 1909 .

[27]  William P. Birmingham,et al.  Algorithms for Chordal Analysis , 2002, Computer Music Journal.

[28]  Tom Collins,et al.  Improved methods for pattern discovery in music, with applications in automated stylistic composition , 2011 .

[29]  Ian Knopke,et al.  A System for Identifying Common Melodic Phrases in the Masses of Palestrina , 2009 .

[30]  Vasili Byros,et al.  Meyer's Anvil: Revisiting the Schema Concept , 2012 .

[31]  María Herrojo Ruiz,et al.  Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation , 2010, NeuroImage.