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Composer attribution by quantifying compositional strategies
Peter van Kranenburg
Department of Information and Computing Sciences
Utrecht University
petervk@cs.uu.nl
Abstract
Taking a theory of musical style developed by Leonard B.
Meyer as a starting point, an experiment is described in
which statistical pattern recognition algorithms are used to
characterize a particular musical style with respect to other
styles. The resulting description can be used in authorship
discussions. In the current study, a number of disputed or-
gan works from the Bach catalogue is used to illustrate the
possibilities of this approach.
Keywords: Musical Style, Pattern Recognition, Classical
Music, Composer Attribution, Johann Sebastian Bach.
1. Introduction
In order to describe a musical style or differences between
styles or the historical development of certain styles, a the-
ory of style is necessary. This applies to “traditional” de-
scriptions of musical style as well as studies in which tools
and algorithms from information technology are used.
In [5], Leonard Meyer develops a theory of musical style
that can be used as starting point for studies in which sta-
tistical pattern recognition algorithms are used to study and
compare musical styles. Meyer defines (musical) style as
follows: Style is a replication of patterning, whether in hu-
man behavior or in the artifacts produced by human behav-
ior, that results from a series of choices made within some
set of constraints.
Without repeating patterns, there would be no style at
all. The constraints are important for they shape a musi-
cal style by allowing certain patterns and disallowing oth-
ers. Meyer distinguishes three levels in these constraints:
Laws, rules and strategies. Laws are universal constraints,
e.g., One cannot ask a piccolo to play a contra G. The sec-
ond level, the rules are intracultural constraints. It is in the
rules that music from the Renaissance differs from music
from the Baroque. The third level, the strategies are con-
straints the composer subjects himself to, within the rules of
a certain cultural established style. Thus it is in the strate-
gies that the music of G.F. Handel differs from the music of
G.Ph. Telemann. Strategies reside on conscious as well as
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°2006 University of Victoria
on unconscious levels. Certain patterns are ingrained dur-
ing the training and development of a composer and are not
replicated consciously.
In the second part of his book, Meyer applies his the-
ory to nineteenth century western classical music. He ad-
dresses some general patterns that recur in many composi-
tions from that age and connects these patterns to the un-
derlying romantic esthetic and ideology. In doing so, he is
forced to limit himself to proof by example. For a more pro-
found evaluation of musical styles, it would be necessary to
make extensively use of all available data (i.c. everything in
all considered scores). For achieving this, statistical pattern
recognition algorithms can be of great use. As Meyer him-
self states: “Since all classification and all generalization
about stylistic traits are based on some estimate of relative
frequency, statistics are inescapable.” ([5], p. 64).
2. A Pattern Recognition Approach
Meyer’s theory offers a foundation for the design of exper-
iments in which algorithms from statistical pattern recog-
nition are used. The features that will represent (parts of)
compositions can be allied with the replicated patterns that
are mentioned in Meyer’s definition. Assuming that for a
certain musicological problem the scores involved are elec-
tronically available, a major task will be the extraction of
the feature values from those scores. From the perspective
of “traditional” style analysis, large-scale features are more
interesting than small-scale features, e.g., in order to deter-
mine the way in which a certain composition resembles a
sonata-form, a global overview of the entire composition
is necessary. From the perspective of algorithmic extrac-
tion, small-scale features are more interesting, because the
algorithms to extract them are less complicated and the re-
sults less ambiguous. It is, for example, not clear how to
quantify the extent to which a composition resembles a cer-
tain sonata-form, but it is much less difficult to determine
the proportion of parallel thirds with respect to all inter-
val successions in the composition. So we need small-scale
patterns, which can be easily detected and counted, and of
which we have many.
With the limitation of the previous section in mind, a
set of twenty features is designed. The smallest scale in
a score is that of the relation of a single note to the other
notes around it. When a single note is part of a voice in a
polyphonic composition, it is more independent than when
it is part of a chord. Because of this, most features quan-
tify aspects of the relations between the different voices,
which means that only polyphonic compositions can be rep-
resented with the designed feature set. Since we will use this
representation for studying authorship of organ fugues, this
is not a problem. There are also some other features in the
set, that describe more global characteristics. The features
are described in [1]. Here a list of them is provided:
1. StabTimeslice 6. PitchEntropy 11. PartAugFourths 16. PartOctaves
2. DissPart 7. VoiceDensity 12. PartDimFifths 17. ParThirds
3. BeginBarDiss 8. PartSeconds 13. PartFifths 18. ParFourths
4. SonorityEntropy 9. PartThirds 14. PartSixths 19. ParSixths
5. HarmonyEntropy 10. PartFourths 15. PartSevenths 20. StepSuspension
By measuring all these features, compositions are repre-
sented as vectors in a 20-dimensional space. To such a data
set various kinds of pattern recognition algorithms can be
applied.
3. Organ Fugues ascribed to J.S. Bach
As a pilot experiment, a data set is assembled with 16 fugues
for organ that are listed in the catalogue of compositions of
Johann Sebastian Bach ([7]). Of six of these fugues the au-
thorship has been disputed. Also five fugues of his eldest
son, Wilhelm Friedemann Bach, and eight of his most im-
portant student, Johann Ludwig Krebs, are incorporated. So
we have a three-class data set.1Each composition is seg-
mented using a segmenting method described in [1], so each
composition is represented by a ’“cloud” of points.
The Fisher-transformation (described in [8], p. 145ff) can
be used to project the data points onto a two-dimensional
space in such a way that the classes are optimal separated
(figure 1). This projection shows that the compositions of
each composer do form a cluster. So it can serve as a refer-
ence for classifying compositions which might be composed
by be one of the three involvedcomposers.
Figure 1 indicates where the data points of the disputed
fugues are projectet. Some interesting observations can be
made. The F minor fugue BWV 534, is projected among
the fugues of J.L. Krebs. This fugue has been ascribed to
W.F. Bach ([3]). With the current result, that ascription can
be rejected. An ascription to J.L. Krebs seems more likely.
A suggested composer for BWV 536 is J.P. Kellner ([4]).
If this is true, Kellners style resembles more the style of
J.S. Bach than that of the other two composers. BWV 537
is said to be composed partly by J.S. Bach (bar 1–40) and
partly by J.L. Krebs ([6]). The first part is projected among
the works of J.S. Bach indeed. The second part however, is
outside of both the Bach-region and the Krebs-region. The
ending of the fugue is in the region between J.S. Bach and
Krebs. This does not fully support the hypothesis, but it
shows that a large part of the fugue is not Bach-like. Also
Bach’s authorship of the fugue in C minor, BWV 546, has
1The dataset is available from: http://www.musical-style-
recognition.org.
Figure 1. Projection of disputed fugues on top of the the com-
positions of J.S. Bach (+), W.F. Bach (o) and J.L. Krebs (*).
been doubted ([2]). The current evaluation shows us that,
with respect to the styles of W.F. Bach and J.L. Krebs, this
fugue has the characteristics of the style of J.S. Bach. The
most famous organ work in existence, the toccata and fugue
in D minor, BWV 565, is not projected among the other
compositions of Bach. This confirms the doubts expressed
in [9].
4. Conclusion
Although the current results don’t offer enough evidence to
draw conclusions about the authorship of the involved com-
positions, it is clear that the proposed method is very help-
ful in finding hypotheses about differences in personal styles
and thus for studying authorship problems.
References
[1] E. Backer and P. van Kranenburg, “On musical stylometry––
a pattern recognition approach”, in Pattern Recognition Let-
ters, 26 (2005), 299–309.
[2] W. Breig, “Versuch einer Theorie der Bachschen Orgelfuge”,
in Die Musikforschung 48 (1995), 14–52.
[3] P. Dirksen, “Het auteurschap van Praeludium en fuga in f
(BWV 534)”, in Het Orgel 96 (2000), nr. 5, 5–14.
[4] D. Humphreys, “A Bach Polyglot––the A major Prelude &
Fugue BWV 536”, in The Organ Yearbook XX (1989), 72–
87.
[5] L.B. Meyer, Style and Music–– Theory, History, and Ideol-
ogy, Chicago, 1989.
[6] J. O’Donnell, “Mattheson, Krebs and the Fantasia & Fugue
in C minor BWV 537”, in The Organ Yearbook XX (1989),
88–95.
[7] W. Schmieder, Thematisch-systematisches Verzeichnis der
musikalischen Werke von Johann Sebastian Bach. Bach-
Werke-Verzeichnis–– 2. ¨
uberarbeitete und erweiterte Aus-
gabe, Wiesbaden, 21990.
[8] A. Webb, Statistical Pattern Recognition, Chichester, 22002.
[9] P. Williams, “BWV 565: a toccata in D minor for organ by
J. S. Bach?”, Early Music 9 (1981), 330–337.
