Journal of New Music Research
2010, Vol. 39, No. 4, pp. 363–377

The Magaloﬀ Project: An Interim Report
Sebastian Flossmann1, Werner Goebl2, Maarten Grachten3, Bernhard Niedermayer1, and Gerhard Widmer1,41

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Johannes-Kepler-University Linz, Austria; 2University of Music and Performing Arts Vienna, Austria; 3Ghent
University, Belgium; 4Austrian Research Institute for Artiﬁcial Intelligence (OFAI), Austria

Abstract
One of the main diﬃculties in studying expression inmusical performance is the acquisition of data. Whileaudio recordings abound, automatically extracting precise information related to timing, dynamics, andarticulation is still not possible at the level of precision
required for large-scale music performance studies. In
1989, the Russian pianist Nikita Magaloﬀ performed
essentially the entire works for solo piano by Frédéric
Chopin on a Bösendorfer SE, a computer-controlled
grand piano that precisely measures every key and pedal
action by the performer. In this paper, we describe theprocess and the tools for the preparation of thiscollection, which comprises hundreds of thousands ofnotes. We then move on to presenting the results ofinitial exploratory studies of the expressive content of thedata, speciﬁcally eﬀects of performer age, performanceerrors, between-hand asynchronies, and tempo rubato.
We also report preliminary results of a systematic studyof the shaping of particular rhythmic passages, using thenotion of phase-plane trajectories. Finally, we brieﬂy
describe how the Magaloﬀ data were used to train aperformance rendering system that won the 2008 Rencon
International Performance Rendering Contest.

1. Introduction
By now there is a substantial history of quantitative,
computer-based music performance research (e.g.
Clarke, 1985; Palmer, 1989, 1996a, 1996b; Repp, 1995,
1992; Gabrielsson, 1999, 2003; Clarke & Windsor, 2000;
Goebl, 2001; Repp, Windsor, & Desain, 2002; Honing,
2003; Widmer, Dixon, Goebl, Pampalk, & Tobudic,

2003; Widmer & Goebl, 2004; Windsor, Desain, Penel, &Borkent, 2006—to name but a few). The main diﬃculty is
the acquisition of representative data—preferably a large
amount of precise information by high-class artists underconcert (not laboratory) conditions. The Magaloﬀ
project is centred around such a resource: the Magaloﬀ
Chopin corpus, recordings of the Russian pianist Nikita
Magaloﬀ publicly performing the complete works forsolo piano by Frédéric Chopin on stage, at the Vienna
Konzerthaus in 1989. The collection meets all of theabove-mentioned criteria: it comprises over 150 pieces,over 10 hours of playing time, over 330,000 played notes.
Having been performed and recorded on a Bösendorfer
SE computer-controlled grand piano, the precise
measurements of timing, loudness, etc. of each playednote along with the pedal movements are available. To
the best of our knowledge, as such it is the ﬁrst precisely
documented comprehensive collection of the complete
works of a composer performed by a single artist as wellas the largest collection of performances of a single
artist available for performance research. By special
permission of Magaloﬀ’s widow we are allowed to use
this data for our research.
For further use of the corpus, it is necessary toannotate the raw MIDI data with the correspondingscore information. That includes converting the musicscore sheets into a machine readable format and aligning
the score with the performance (see Section 3). The result
constitutes the Magaloﬀ corpus, the empirical foundationand ﬁrst milestone of our project. Based on this data, weseek new insights into the performance strategies appliedby an accomplished concert pianist. In Section 4, we
describe several research strands that are currently
pursued and present some ﬁrst preliminary results related
to several aspects of performance: the eﬀects of age andCorrespondence: Sebastian Flossmann, Johannes-Kepler-University, Department of Computational Perception, Altenbergerstrasse 69,
Linz, 4040 Austria. E-mail: sebastian.ﬂossmann@jku.at
DOI: 10.1080/09298215.2010.523469 Ó 2010 Taylor & Francis

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how Magaloﬀ copes with it; the phenomenon of
performance errors; the use of between-hand asynchronies as an expressive device, and especially tempo rubato.
We also describe ﬁrst results of a systematic study of thetemporal shaping of particular rhythmic passage, using
the notion of phase-plane trajectories. In Section 5,
ﬁnally, we discuss the use of the Magaloﬀ corpus astraining data for a performance rendering system that
won the 2008 Rencon International Performance Rendering Contest in Japan.

2. Nikita Magaloﬀ
2.1 Biographical remarks
Nikita Magaloﬀ, born on 21 February 1912, in St
Petersburg, was a Russian pianist. As his family wasfriendly with musicians like Sergei Rachmaninov, Sergei
Prokoﬁev and Alexander Siloti, he grew up in a very
musical environment. In 1918, the family ﬁrst moved to
Finland and then to Paris soon after (1922), where Nikita
Magaloﬀ started studying piano with Isidore Philipp,graduating from the Conservatoire in 1929 (Cella &Magaloﬀ, 1995).
Magaloﬀ started his professional career mainly inGermany and France, often appearing together with theviolinists Jószef Szigeti (whose daughter Irène he later
married) and Arthur Grumiaux, and the cellist Pierre
Fournier. In 1949, he took over Dinu Lipatti’s piano
class at the Geneva Conservatoire where he continued
teaching untill 1960. His pupils include Jean-Marc
Luisada, Maria Tipo, Sergio Calligaris, Michel Dalberto
and Martha Argerich.
Magaloﬀ is especially known for his performances of
the complete works of Frédéric Chopin, which he usuallypresented live in a cycle of six recitals. The ﬁrst everrecording of the complete works of Chopin was made by
Magaloﬀ in the years 1954–1958 for Decca. He repeatedthis for Philips in 1975. Other than that, only a few studiorecordings by Magaloﬀ exist. Nikita Magaloﬀ died on 26
December 1992, at the age of 80 in Vevey, in the Canton
Vaud in Switzerland (Cella & Magaloﬀ, 1995).
2.2 Magaloﬀ’s Vienna concerts in 1989
Between 1932 and 1991, Magaloﬀ appeared in 36 concertsin the Wiener Konzerthaus, one of Vienna’s mostillustrious concert venues—24 solo concerts, 10 concerts
as orchestra soloist, two chamber recitals together withJózsef Szigeti.1 In 1989, he started one of his famous
Chopin cycles in which he would play all Chopin’s works

for solo piano that were published in the composer’s
lifetime, essentially Op. 1 to Op. 64, in ascending order.
Each of the six concerts was concluded with an encore
from the posthumously published work of the composer.
The concerts took place between 16 January and 17 May
1989, in the Mozartsaal of the Wiener Konzerthaus. At thetime of the concerts, Magaloﬀ was already 77 years old.
Daily newspapers commenting on the concerts praiseboth his technique and his unsentimental, distant way ofplaying (Sinkovicz, 1989; Stadler, 1989). Table 1 lists the
programs of the six concerts.
Although the technology had only been invented a shorttime before (ﬁrst prototype in 1983, oﬃcial release 1985
(Moog & Rhea, 1990)), all six concerts were played andrecorded on a Bösendorfer SE, precisely capturing every
single keystroke and pedal movement.2 This was probably
the ﬁrst time the new Bösendorfer SE was used to such an
extent. The collected data is most likely the mostcomprehensive corpus every recorded from one performer.
In 1999, we received written and exclusive permission by
Irène Magaloﬀ, Nikita Magaloﬀ’s widow, to use the datafor our research.

3. Preparation of the corpus
The recorded symbolic performance data requires careful
preparation to become accessible for further investigations. Without any reference to the score, nothing can besaid about how speciﬁc elements were realized. A lengthened eighth note and a shortened quarter note may account for the same amount of performed time, the former
probably being part of a slower passage in the same piece.
Without any information about the notated duration of
the note, no assumption can be made about what kind of
modiﬁcation the performer applied to the note.
In the following we describe the steps we undertook toprovide the score information for all performed notes—arather demanding challenge, which took more or less awhole person year. We need the ﬁnal state for the corpus
to be a piecewise list of all performed notes aligned with
their counterparts in the score. For this we ﬁrst needsymbolic, computer-readable representations of allscores, which then are aligned to the MIDI data
representing Magaloﬀ’s performances. Given the natureof Chopin’s music—high note density, high degree of
expressive tempo variation—automatic matching will beerror-prone and accordingly, intensive manual correctionof the alignment is required. As the most intuitive way toview a score is the music score itself, the easiest access formanually inspecting and correcting an alignment is to2

1

Information available through the program archive oftheWienerKonzerthaus,
http://konzerthaus.at/archiv/
datenbanksuche

Each note on- and oﬀset is captured with a temporal
resolution of 1.25 ms. The velocity of the hammer at impact
is converted and mapped to 128 midi loudness values. See
Goebl and Bresin (2003) for details.

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Table 1. The Magaloﬀ Konzerthaus Concerts 1989.
Date

Played16 JanRondo Op. 1; Piano Sonata No. 1 Op. 4; Rondo Op. 5; 4 Mazurkas Op. 6; 5 Mazurkas Op. 7; 3 Nocturnes Op. 9; 12Études Op. 10.
Encore: Fantaisie-Impromptus Op. posth. 66.

19 JanVariations Op. 12; 3 Nocturnes Op. 15; Rondo Op. 16; 4 Mazurkas Op. 17; Grande Valse Op. 18; Bolero Op. 19;
Scherzo No. 1 Op. 20; Ballade No. 1 Op. 23; 12 Études Op. 25.
Encore: Variations ‘Souvenir de Paganini’ (posth.)

15 Mar2 Polonaises Op. 26; 2 Nocturnes Op. 27; 24 Preludes Op. 28; Impromptu No. 1 Op. 29; 4 Mazurkas Op. 30;
Scherzo No. 2 Op. 31.
Encore: Waltz in E minor (posth.)

10 Apr

2 Nocturnes Op. 32; 4 Mazurkas Op. 33; 3 Waltzes Op. 34; Piano Sonata No. 2 Op. 35; Impromptu No. 2 Op. 36; 2
Nocturnes Op. 37; Ballade No. 2 Op. 38; Scherzo No. 3 Op. 39; 2 Polonaises Op. 40; 4 Mazurkas Op. 41;
Waltz Op. 42; Tarantella Op. 43.
Encore: Waltz E[ major (posth.)

13 AprPolonaise Op. 44; Prelude Op. 45; Allegro de Concert Op. 46; Ballade No. 3 Op. 47; 2 Nocturnes Op. 48;
Fantaisie Op. 49; Impromptu No. 3 Op. 51; 3 Mazurkas Op. 50; Ballade No. 4 Op. 52; Polonaise Op. 53; Scherzo
No. 4 Op. 54.
Encore: Ecossaises Op. posth. 72 No. 3.

17 May2 Nocturnes Op. 55; 3 Mazurkas Op. 56; Berceuse Op. 57; Piano Sonata No. 3 Op. 58; 3 Mazurkas Op. 59; BarcarolleOp. 60; Polonaise-Fantaisie Op. 61; 2 Nocturnes Op. 62; 3 Mazurkas Op. 63; 3 Waltzes Op. 64.
Encore: Waltz Op. posth. 69 No. 1

display the score page and the piano roll representationof the performance (MIDI) joined together by thealignment. This requires a score representation that
contains not only information pertaining to the musicalcontent of the piece but also to the geometrical locationof each and every element on the original printed score.
The format most suitable for our needs is musicXML(Recordare, 2003). MusicXML is intended to describe all
information—musical content, expressive annotations,editorial information—contained in a score and is also
very commonly used in optical music recognition (OMR)
software. As it is text-based and human readable, it iseasy to extend the format with the geometrical information we need.
3.1 From printed score to extended MusicXML
The ﬁrst step in digitizing the score is to scan the sheetmusic. As we have no information as to which score
editions Magaloﬀ used, we used the Henle Urtext
Editions (Zimmermann, 2004) with the exceptions of
the Sonata Op. 4 and the Rondos Op. 1, Op. 5 and Op.
16, which Henle does not provide; in these cases we wereforced to use the obsolete Paderewsky editions (Paderewski, 1999, 2006). The 930 pages of sheet music were
scanned in greyscale with a resolution of 300 dpi. The
commercial OMR software SharpEye3 was used to3

See http://www.visiv.co.uk

extract the musical content from the scanned sheets.
Figure 1 shows a screenshot of the program working on
Chopin’s Ballade Op. 52.
The example illustrates several problems in therecognition process: the middle voice starting in the
beginning of the second bar (B[4) is misinterpreted as aseries of sixteenth notes instead of eighths, which is easy
to miss both when reviewing the score as well as listening
to a mechanical MIDI rendering. The middle voice in thesecond half of the bar could not be read from the scanand has to be added manually. To emphasize a melody
voice or to clarify a situation where voices cross, a note
may have two stems with diﬀerent durations. In the caseshown in Figure 1, the sixteenth notes G4 starting in theﬁrst measure on beat 4 can be interpreted as expressive
annotation or interpretative advice rather than actual
note content. Keeping the ones with the shortest
duration, the duplicated notes had to be removed, as
they would bias the error statistics we carry out on theperformances (see Section 4.2). Other common problems
include 8va lines (dashed lines indicating that certain
notes actually have to be played one octave higher orlower) that are not recognized by SharpEye, bars
spanning more than one line, and certain n-tuplets ofnotes. Especially rhythmically complex situations with
diﬀerent independent voices can lead to problems in theconversion. Figure 2 shows such a situation: a sixteenthrest has to be added so that SharpEye places the B4 on
the correct onset. Thus, intensive inspection and extensive manual corrections have to be made. The graphicalDownloaded By: [Universitat Fur Musik und Darstellende Kunst Wien] At: 08:51 10 May 2011366

Sebastian Flossmann et al.

Fig. 1. The SharpEye OMR Software showing the printed score (lower panel) and the result of the recognition software (upper panel).

musicXML ﬁle, thus linking the musicXML ﬁle with its
original sheet music image.
3.2 Score-performance matching and graphical correction

Fig. 2. A multivoice situation where a rest has to be added sothat the middle voice starting is placed on the correct symbolic
onset (left: score image, right: SharpEye Interface).

alignment software discussed in Section 3.2 provides formanual post-correction of those.
The choice of SharpEye was also motivated by the fact
that, while SharpEye exports the results in a musicXML
format which originally does not store the geometriclocation of the elements on the page, it also provides
access to the intermediate, internal representation of theanalysed page. This information is stored in mro ﬁles,SharpEye’s native ﬁle format. In mro ﬁles, all recognizedelements are described graphically rather than musically:
notes are stored with their position relative to the staﬀ
rather than with a musical interpretation of the note thattakes the clef into account. Figure 3 shows the samechord represented in the two diﬀerent formats. A
custom-made script was used to extract the geometrical
position of the note elements from the mro ﬁle and add
the information to the corresponding elements in the

Score-performance matching is the process of aligning
the score and a performance of a musical piece in such a
way that for each note of the score the corresponding
performed note is marked and vice versa. Each scorenote is either marked as matched or omitted if the scorenote was not played, and each performed note is marked
as either matched or inserted if the played note has nocounterpart in the score. With the exception of trills andsome other ornaments, this constitutes a one-to-one
matching situation of score and performance.
Several matching strategies are mentioned and evaluated in the literature (Heijink, Desain, Honing, &Windsor, 2000; Raphael, 2006), ranging from straightforward matching to dynamic time warping or Hidden
Markov Models. We use the edit-distance paradigm thatwas initially invented for string comparisons (Wagner &Fischer, 1974) and has been used in diﬀerent music
computing applications (Dannenberg, 1984; Pardo &Birmingham, 2002). Grachten (2006) oﬀers more detailed
information on edit-distance-based matching as a scoreperformance alignment algorithm. Since the edit-distanceassumes a strict order of the elements in the sequences tobe aligned, it is not directly applicable to polyphonicmusic. To solve this problem, we represent polyphonicmusic as sequences of homophonic slices (Pickens, 2001),

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The Magaloﬀ project

367

Fig. 3. A chord in the musicXML format (left panel) and its counterpart in the SharpEye mro format (right panel).

by segmenting the polyphonic music at each note onset
and oﬀset. The segments, represented as the set of pitchesat that time interval, have a strict order, and cantherefore be aligned using the edit-distance. A series ofedit operations—insertion, omission, match and trill
operations in our case—then constitute the alignment
between the two sequences. Each of the applied operations comes at a cost (the better the operation ﬁts in a
speciﬁc situation, the lower the cost), the sum of which isminimized over the two sequences—score and performance.
Due to the complexity of the music and the highly
expressive tempo and timing variations in the performances, the automatic score-performance matching isvery error-prone. As the number of notes is vast, theinterface for correcting and adjusting the alignment has
to be intuitive and eﬃcient. Extending the musicXML bygeometric information from the scanning process allows
for an application displaying the original score sheet in
an interactive way: each click on the note elements in theimage can be related to the corresponding entry in themusicXML score. A combined display of this interactivescore and the performance as a piano roll provides easy
access to inspecting and modifying the alignment.
Figure 4 shows a screenshot of the platform independent
Java-Application we developed.
One problem with the matching was that in somepieces there are diﬀerences between our version of thescore and the version performed by Magaloﬀ: this ranged
from small discrepancies where, e.g., Magaloﬀ repeats agroup of notes more often than written in the score (e.g.
in the Nocturne Op. 9 No. 3, bar 111), to several skipped

measures (e.g. Waltz Op. 18, where he omitted bars 85 to116), to major diﬀerences that probably are the result ofa diﬀerent edition being used by Magaloﬀ (e.g. in the
Sonata Op. 4 Mv. 1, bars 82 to 91, where the notes heplays are completely diﬀerent from what is written in thescore). In the error analysis presented in Section 4.2
below, we will not count these as performance errors,
and we also do not count these cases as insertions oromissions in the overview Table 2.
3.3 Statistical overview
Table 2 gives a summary of the complete corpus. Gracenotes and trills are mentioned separately: grace notes do
not have a nominal duration deﬁned by the score.
Therefore they cannot contribute to discussions of
temporal aspects of the performance. As a consequence
we normally exclude those from the data. Trills constitutemany-to-one matches of several performance notes to asingle score note. When counting the performance notes in
the corpus, the number of performance notes matched to atrill have to be accounted for. Accordingly, the complete
number of performed notes is composed of the number ofmatches, substitutions, insertions, matched grace notes,
and trill notes. The complete number of score notes iscomposed of the number of matches, substitutions,omissions, and matched and omitted grace notes.
Table 3 shows the note and matching statistics
according to piece categories. The generic category
Pieces includes: Introduction and Variations Op. 12,
Bolero Op. 19, Tarantella Op. 43, Allegro de Concert
Op. 46, Fantaisie Op. 49, Berceuse Op. 57, BarcarolleDownloaded By: [Universitat Fur Musik und Darstellende Kunst Wien] At: 08:51 10 May 2011368

Sebastian Flossmann et al.

Fig. 4. GraphMatch: a software tool for display and manual correction of score-performance alignments.

Table 2. Overview of the Magaloﬀ corpus.

4.1 Performer age

Pieces/Movements
Score pages
Score notes
Performed notes
Playing time

155
930
328.800
335.54210h 7m 52s

Matched notes
Inserted notes
Omitted notes
Substituted notes
Matched grace notes
Omitted grace notes
Trill notes

318.11212.32511.506
5.105
4289
449
5923

Op. 60, and Polonaise-Fantaisie Op. 61. The encores
were not included in the corpus.

4. Exploratory intra-artist researchThis section describes a number of initial studies we
performed on the data in order to explore characteristicsof Magaloﬀ’s playing style. We view these as ﬁrst steps
into investigating the art of a world-class pianist basedon data with unprecedented precision.

One of the remarkable aspects of Magaloﬀ’s Chopin
concerts in 1989 is the age at which he undertook thisformidable task: he was 77 years old.4 Performing on
stage up to old ages is not exceptional among renowned
pianists: Backhaus played his last concert at 85, Horowitz
at 84, Arrau at 88. The enormous demands posed by
performing publicly include: motor skills, memory,
physical endurance, and stress factors (Williamon,
2004). A psychological theory of human life-spandevelopment identiﬁes three factors that are supposed
to be mainly responsible for ‘successful ageing’: Selection,
Optimization, and Compensation (SOC model—Baltes &Baltes, 1990). Applied to piano performance, this would
imply that older pianists play a smaller repertoire(selection), practice these few pieces more (optimization),and hide technical deﬁciencies by reducing the tempo offast passages while maintaining tempo contrasts between
fast and slow passages (compensation) (Vitouch, 2005).
In Flossmann, Goebl, and Widmer (2009), we tested
whether Magaloﬀ actually used strategies identiﬁed in the
SOC model.
The ﬁrst aspect of the SOC model, selection, seems not
to be supported in this case: Magaloﬀ performed the4

At age 77, Alfred Brendel performed one solo program andone Mozart Concerto for his last season in 2008.

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The Magaloﬀ project
Table 3. Overview by piece category.

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Category
BalladesÉtudes
Impromptus
Mazurkas
Nocturnes
PiecesPolonaises
PreludesRondosScherzi
SonatasWaltzes

Pieces

Score

PlayedMatches

Insertions

Omissions

Substitutions

4
24
3
41
19
7
7
25
3
4
12
8

19,51140,8947216
47,312
31,10939,75927,87320,067
18,25021,951
38,971
18,651

20,22340,8637310
47,04332,016
41,06828,301
20,23918,331
22,63340,45018,876

18,971
38,6847150
45,26030,94338,24926,232
19,234
17,347
20,84937,015
18,178

1001
1615
96
1129
671
1728
1597
683
324
1369
1651
461

496
1681
159
1669
873
1487
1189
631
441
707
1498
675

251
561
64
470
302
916
436
321
440
376
731
237

entire piano works by Chopin within four months.5 We
cannot make a statement about optimization processes
due to our lack of information about his practice regime
before and during the concert period.
Regarding possible compensation strategies, we studied Magaloﬀ’s performance tempi in the context ofother recordings—on the Études only, to keep the eﬀort
manageable. We analysed selected recordings of Chopin’s Études by several renowned pianists, including an
earlier recording by Magaloﬀ at the age of 63. These
audio recordings, a total of 289 performances of 18
Études by 16 performers,6 were semi-automatically beattracked using the software Beatroot (Dixon, 2001, 2007)
to determine a tempo value.7
Compared to these performances, Magaloﬀ’s Op. 10
Études are on average 1.2% slower, the Op. 25 Études
5.6% slower than the average performance. Comparedwith the metronome markings in the Henle editions, 12
out of 18 of Magaloﬀ’s performances are within a 10%
range, three pieces more than 5% slower, three pieces
more than 5% faster. Comparing Magaloﬀ’s recordings
at the age of 63 and 77, the tempi vary to a surprisingdegree, but no systematic tempo decrease in the latter
could be found. On the contrary, in 12 pieces out of 18,

5
Of course, Magaloﬀ’s repertoire might have been broader inyounger years, which would then indicate otherwise. A
systematic comparison of earlier concert seasons and allconcert in 1989 would provide further insights into thatparticular aspect.
6
Arrau (recorded 1956), Ashkenazy (1975), Backhaus (1928),
Biret (1990), Cortot (1934), Gavrilov (1985), Giusiano (2006),
Harasiewicz (1961), Lortie (1986), Lugansky (1999), Magaloﬀ(1975), Magaloﬀ (1989), Pollini (1972), Schirmer (2003),
Shaboyan (2007), and Sokolov (1985).
7
A basic tempo value was estimated by the mode value, themost frequent bin of an inter-beat interval histogram with a bin
size of 4% of the mean inter-beat interval.

the recording at age 77 is faster, sometimes to aconsiderable degree (up to 17% in Op. 10 No. 10). On
the whole, Magaloﬀ’s performances do not suggest acorrelation between age and tempo, while the tempi ofthe other pianists’ recordings show a slight age eﬀect(with piecewise correlations between pianist age andtempo ranging from 70.66 to 0.51, with an average of
70.17).8
As an exemplary piece containing tempo contrasts, we
examined the Nocturne Op. 15 No. 1 (Andante cantabile),
which contains a technically demanding middle section(con fuoco). The tempo values of performances by 14
other pianists, including Argerich, Rubinstein and
Pollini, show a signiﬁcant correlation between the ageof the performer at the time of the recording, and thetempo of the middle section (the older, the slower). The
tempo ratios between the contrasting sections, however,showed no overall age eﬀect, conﬁrming Vitouch’s
interpretation of the SOC model (Vitouch, 2005).
Magaloﬀ’s performance of the Nocturne does not fall
into this pattern: he played faster than the youngest of
the performers while keeping a comparable tempo ratio.
Thus, our analysis of Magaloﬀ’s tempi does not point
to any compensation processes, which were indeed foundwith other pianists. In sum, Magaloﬀ’s Chopin does notseem to corroborate the SOC model.
4.2 Error analysis
Performance errors occur at all levels of proﬁciency.
Studies have been conducted under laboratory conditions and give ﬁrst insights into the phenomenon (e.g.
Palmer & van de Sande, 1993, 1995; Repp, 1996).
8

These considerations are based on the underlying assumptionthat the diﬃculty of a piece increases with the tempo. This is notuniversally true. However, for the pieces in question—the fastpieces of the Études—the assumption seems warranted.

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370

Sebastian Flossmann et al.

However, conﬁrming these results under real concert
conditions has been diﬃcult so far. In Flossmann, Goebl,
and Widmer (2009, 2010) we analyse Magaloﬀ’s performance errors, put them into context of both performanceand score and test whether the ﬁndings corroborate
previous studies.
As can be derived from Table 2 the Magaloﬀ performances contain 3.67% insertion errors, 3.50% omission
errors and 1.55% substitution errors. This exceeds thepercentages Repp found (1.48%, 0.98%, and 0.21%,
respectively (Repp, 1996)), but looking only at the particular piece used by Repp (Prelude Op. 28 No. 15) the error
percentages are similar (0.72%, 1.58%, and 0.52%, respectively). Among the piece categories, the Scherzi and
Polonaises stand out in terms of insertion errors (above5%), the Rondos and Impromptus constitute the lowinsertion categories (insertion rate below 2.0%). The Impromptus are also the category with the lowest percentage ofomission errors (2.20%), while Études and Polonaises
exhibit the highest percentage of omission (above 4%).
Considering the errors in the context of the general
tempo of a piece, we found that a high note density goesalong with a higher error frequency (the more notes pertime unit, the more errors). This holds to a varying
degree for all kinds of errors: overall the corpus exhibits
correlation coeﬃcients between note density and frequency of insertion errors, omission errors and substitution errors of 0.39, 0.26 and 0.61, respectively. Figure 5
shows the error rates and correlation coeﬃcients of error
frequency and note density for the respective categories
of pieces. The Ballades and Polonaises show both higherror percentages as well as a high correlation of error
frequency and note density, suggesting that these aretechnically particularly demanding.
The perceptual discernibility of an insertion or asubstitution error is closely related to how loud the wrong

note was played in proportion to the other notes in thevicinity and how well the note ﬁts into the harmonic
context (Repp, 1996). Viewing the insertion notes in the
corpus in their vertical and horizontal context reveals thatthe majority of notes are inserted with at most 70% of theloudness of the adjacent (horizontal and vertical) notes.
An analysis of the harmonic appropriateness of theinsertion and substitution notes in their context, however,suggests that the errors are perceptually more conspicuousthan assumed: 40% of the respective errors are notcompatible with the local harmony.
Our ﬁndings in this live performance data mostly
corroborate Repp’s ﬁndings under laboratory conditions(Repp, 1996): the percentage of errors in melody voices islower than in non-melody voices (omission rates of 1%(melody voices) and 4.1% (non-melody voices)), and the
majority of insertion errors are of low intensity comparedto their immediate neighbourhood. The error frequency
is related to a varying degree to the note density,
depending on the technical demands of the actual piece.
If we may make a somewhat speculative comment
here, the fact that Magaloﬀ did not reduce his
performance tempi even at age 77 (see Section 4.1) andthat his performances display relatively high error rates
might be taken as an indication that Magaloﬀ aimed atrealizing his musical ideas of Chopin’s work rather thanerror-free performances. Further analyses will try toestablish connections between score characteristics andcertain error patterns.
4.3 Between-hand asynchronies
Temporal oﬀsets between the members of musicalensembles have been reported to yield speciﬁc characteristics that might reﬂect expressive intentions of theperformers; e.g. the principal player in wind or string

Fig. 5. Left panel: error percentages by piece category. Right panel: correlation coeﬃcients between note-density and error rate bypiece category.

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The Magaloﬀ project
trios precedes the others by several tens of milliseconds(Rasch, 1979), and soloists in jazz performances havebeen shown to synchronize with the rhythm section at
oﬀbeats (Friberg & Sundström, 2002). As the hands of a
pianist are capable of producing diﬀerent musical parts
independently, the temporal asynchronies between thehands may be an expressive means for the pianist. In
Goebl, Flossmann, and Widmer (2010), we examined
the between-hand asynchronies in the Magaloﬀ corpus.
The asynchronies were computed automatically over theentire corpus based on staﬀ information contained in
the score, assuming that overall the right hand played theupper staﬀ and the left hand the lower. For the analysisof this phenomenon we excluded all onsets marked in thescore as arpeggiated; in these cases temporal deviations
are prescribed by the score rather than being part of theinterpretation. The main results of this study (Goeblet al., 2010) are reported brieﬂy in the following.
The analysis of over 160,000 nominally simultaneous
events revealed tempo eﬀects: slower pieces were playedby Magaloﬀ with larger asynchronies than faster pieces.
Figure 6 (left panel) shows the correspondence between
event rate and asynchrony. Moreover, pieces withchordal texture were more synchronous than pieces withmelodic textures. Subsequent analyses focused on speciﬁc
kinds of between-hand asynchronies: bass anticipations
and occurrences of ‘tempo rubato in the earlier meaning’(Hudson, 1994).
As bass anticipations we consider events where a bass
note precedes the other voices by more than 50 ms. They
can be clearly perceived due to their large asynchronies
and can be considered to be expressive decisions by theperformer. Magaloﬀ’s performances contain a considerable number of these bass anticipations (about 1% of all371

simultaneous events). Again, higher proportions arefound in slower pieces.
The ‘tempo rubato in the earlier meaning’ refers toparticular situations in which the right hand deviates
temporally from a stable timing grid established by theleft hand (Hudson, 1994). Chopin, in particular, recommended to his students this earlier type of rubato as
opposed to the later type that refers to a parallel slowing
down and speeding up of all parts of the music (today
referred to as expressive timing). We automatically
identify sequences where Magaloﬀ apparently employedan ‘earlier tempo rubato’ by searching for out-of-sync
regions in the pieces. An out-of-sync region is deﬁned as asequence of consecutive asynchronies that are larger
than the typical perceptual threshold (30 ms) and thatcomprises more events than the average event rate of thatpiece. On average, 1.8 such regions were found per piece(283 in total) with particularly high counts in the
Nocturnes—a genre within Chopin’s music that leaves
most room for letting the melody move freely above theaccompaniment. Figure 6 shows the correspondence
between event density and number of ‘earlier tempo
rubato’ sequences.
Finally, an attempt was made to predict Magaloﬀ’s
asynchronies on the basis of a set of mostly local score
features using a probabilistic learning model. Betweenhand asynchronies in some individual pieces could be
predicted quite well (Étude Op. 25 No. 11 or Impromptu
Op. 29), but generally the prediction results were poor. It
might be that a more complex representation of the score
might be required to explain and predict between-handasynchronies, which potentially contain a range of
expressive intentions in Magaloﬀ’s Chopin (Goeblet al., 2010).

Fig. 6. Left panel: absolute asynchronies plotted against the mean event rate by piece category. Right panel: the number of out-of-sync
regions plotted against the mean event rate plotted.

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4.4 Phase-plane representations for visual analysisof timing
In this section, we illustrate how phase-plane representations of timing data provide a tool for exploring andunderstanding various aspects of the data. The phaseplane representations are a visualization tool common inphysics and dynamic systems theory. It was introduced in
the context of music performance research (Ramsay,
2003; Grachten, Goebl, Flossmann, & Widmer, 2008,2009), mainly because of its emphasis on dynamic aspectsof the data. This is of particular relevance for the analysisof ‘expressive gestures’, which are (at least partially)
manifest as ﬂuctuations in timing and loudness of
performed music.
A phase-plane plot of expressive timing displays measured tempo data as a trajectory in a two-dimensional
space—the state-space—where the horizontal axis represents tempo and the vertical axis represents the ﬁrst
derivative of tempo. Passages of constant tempo do notcause any motion through the state-space, but changes in
tempo lead to (typically curved) clockwise trajectories,
where accelerandi correspond to motion through the ﬁrst
and second quadrants, and ritardandi to motion throughthe third and fourth quadrants.
The phase-plane representation of empirical data ispart of a larger methodology known as functional data
analysis (Ramsay & Silverman, 2005). The core of thismethodology is the construction of a suitable functional
approximation of measured data points, which areassumed to be measurements of some continuous process.
In our case the functional approximation is done using
linear combinations of piecewise polynomial curves (Bsplines) to ﬁt the data. The ﬁtting process uses a leastsquares criterion that includes a penalty term for roughness—thus higher penalties lead to smoother curves.
Phase-plane plots of data are obtained by plotting the
ﬁtted function against its derivative. Derivatives can easily
be computed due to the piece-wise polynomial form. Moredetails can be found in Grachten et al. (2009).
The method for computing phase-planes used here
diﬀers slightly from the one presented in Grachten et al.
(2009). Rather than approximating tempo data, which
are derived from measured onset times in the performance, we ﬁt the measurements directly as a scoreperformance time-map. This method is more robust in
the sense that the ﬁtted function is less susceptible toovershoot due to ﬁtting with low roughness-penalties.
The inter-onset interval (IOI) curve is obtained by taking
the ﬁrst derivative of the function ﬁtted to the scoreperformance time-map. Rather than converting the IOI
curve into a tempo curve for phase-plane display, we
compute the phase-plane trajectory by taking thenegative logarithm of the IOI curve, where the IOIvalues are divided by the average IOI value over theregion of interest. The resulting curve is very similar

conceptually to a tempo curve (that is, greater values
imply a faster tempo), with the diﬀerence that the scale islogarithmic. Thus, a value of 1 corresponds to doubling
the nominal tempo, and a value of 71 to half thenominal tempo.
A data set like the Magaloﬀ corpus oﬀers a unique
opportunity to study how expressive patterns relate tomusical structure. Typical corpora of music performances are less suited for such studies, since they tend
to contain performances by many pianists, but for a
relatively small amount of musical material. Since theMagaloﬀ corpus contains virtually all of Chopin’s piano
works, there is an abundance of musical material.
As a preliminary study of Magaloﬀ’s style ofexpressive timing, we investigate timing patterns corresponding to particular rhythmical contexts throughout
the corpus. The ﬁrst step is to select rhythmical contexts
that occur frequently in diﬀerent pieces. We thencompare the expressive timing data corresponding to
all instances of those rhythmical contexts to see whether
the rhythmical contexts can be characterized by a typicaltiming pattern. We restrict a rhythmical context to be ofﬁxed length, namely one measure. The context is
uniquely determined by its time signature and the onset
times of the left hand (relative to the start of themeasure). Table 4 shows two such rhythmical contexts
and their occurrences in the corpus. Note that thepatterns are both regular divisions of the measure into 16
equal parts, the only diﬀerence being that one pattern has
a 2/2 time signature, and the other 4/4.
Figure 7 shows the phase-plane trajectories corresponding to the patterns A and B. In order to avoid
clutter not all instances of both patterns have been drawn
in the plots. Instead, we show the average trajectory(bold line), together with the average trajectories offour clusters within the set of trajectories (thin lines), in
order to give an impression of the variability within apattern.9
Both patterns show roughly circular trajectories,
indicating a speeding up in the ﬁrst half of the measure
and a slowing down in the second half of the measure.
Although both patterns are quite similar, as might be
expected based on the similarity of the rhythmical
contexts, there are also two clear distinctions. Firstly,
the absolute sizes of the (averaged) trajectories diﬀer
between patterns A and B. Pattern A shows larger
trajectories than pattern B, implying greater ﬂuctuationof tempo. Secondly, pattern B shows an embedded cyclic
form halfway through the trajectories. This corresponds
to a brief slowing down and speeding up in the middle ofthe measure, and suggests that the weak metricalemphasis on the third beat is accentuated by a slight

9

Note that the clustering was not done for any analytical
purpose, only to summarize the trajectory data succinctly.

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Table 4. Two rhythmical contexts, and their occurrences in the Magaloﬀ corpus.
Pattern A

Pattern B

Time signature:

2
2

4
4

Onset Pattern:

1 2 3 4 5 6 7; ; ; ; ; ; ;8 8 8 8 8 8 8
1 2 3 4 5 6 7
1; 1 ; 1 ; 1 ; 1 ; 1 ; 1 ; 1
8 8 8 8 8 8 8

1 2 3
1
2
3
;
;
; 1; 1 ; 1 ; 1 ;16 16 16
16 16 16
1
2
3
1
2
3
2; 2 ; 2 ; 2 ; 3; 3 ; 3 ; 316 16 16
16 16 16

Occurrences:Op. 10, No. 12, Étude (46 times)
Op. 25, No. 6, Étude (4 times)
Op. 28, No. 16, Prelude (2 times)
Op. 28, No. 3, Prelude (26 times)
Op. 46, Allegro de Conc. (10 times)

Op. 10, No. 4, Étude (26 times)
Op. 10, No. 8, Étude (17 times)
Op. 16, Rondo (7 times)
Op. 25, No. 1, Étude (4 times)
Op. 46, Allegro de Conc. (4 times)
Op. 58, Mv. 1, Sonata (29 times)
Op. 62, No. 2, Nocturne (8 times)

0;0;Fig. 7. Phase-plane trajectories for the timing of two rhythmical patterns (left: pattern A; right: pattern B). Overall average trajectories
are displayed in bold lines, cluster-average trajectories in thin lines. The uneven beats are numbered and marked by symbols along the
trajectories.

lengthening. This accentuation is completely absent inpattern A.
A last notable aspect of the plots is that the
trajectories are not completely circular. There is a slight,
but apparently systematic, discrepancy between beginning and end points. Although this might be an artifactof averaging, it is likely that the rhythmical contexts arethemselves part of a larger context that has a characteristic timing pattern, since such patterns often span morethan one measure.

4.5 Towards comprehensive inter-artist investigations
While the Magaloﬀ corpus allows us to analyse one
pianist’s playing style in great depth and with highprecision, even more insights can result from comparing Magaloﬀ’s style to the style of other pianists. This,of course, would require more such corpora ofsymbolic data. Unfortunately, in most cases the only
available resource for data by other pianists are audio
recordings.

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Sebastian Flossmann et al.

Manually annotating a large number of audio
recordings is beyond the limits of our resources. Therefore a longer-term goal is to develop a system that can
extract symbolic data from audio recordings. Since thescore that a performance is based on can be assumed tobe known in most cases, the prime task is to identify eachscore note’s position within the audio recording—a
problem known as audio-to-score alignment. Based on
this step further performance parameters, like loudness,
timbre characteristics, etc., can be estimated.
Audio-to-score alignment has been an issue incomputational music research for more than ten years.
By now two competing approaches as well as numerous
variations and improvements have been established. One
technique is to use the Dynamic Time Warping algorithm
in order to align feature sequences computed from audio
as well as the score (Hu & Dannenberg, 2005). The othermethod is to build statistical, graphical models, which
can not only embed the temporal order of note events but
also additional a priori knowledge, like relative notedurations (Raphael, 2006)
While a lot of recent work has focused on real-time
aspects of audio-to-score alignment, in the present
context accuracy is much more crucial. We have recently
introduced a reﬁnement method which was able to extract
onset times more accurately than the human threshold ofrecognition for about 40% of the notes within our test set(Niedermayer, 2009). Although this result is encouraging,it clearly shows that manual post-processing is stillrequired in order to create accurate data.
Given more reliable automatic annotations the prospect is to build corpora of symbolic performance datafor other pianists with a manageable amount of manual
post-processing. Within the course of this long-term
objective the Magaloﬀ corpus will play several roles: (1)the existing transcriptions can serve as ground truth data
for the quantitative evaluation of any alignment system;(2) manual annotations (like separation of the score into
melody-line, bass-line, etc.) within the Magaloﬀ corpus
can be transferred to other performances by means ofalignment; and (3) it serves as a basis for inter-artist
performance analysis once symbolic data describing
other artists’ performances have been generated.

5. The Magaloﬀ corpus as training data forexpressive performance rendering
A data corpus of this dimension and precision is not onlyinteresting for what it shows about the pianist that created
it. The detailed annotation of score information for eachplayed note makes the corpus a valuable asset as groundtruth data for various data driven music processing tasks.
One such task is Expressive Performance Rendering—the
problem of automatically generating a performance of agiven musical score that sounds as ‘human’ and ‘natural’as possible. To this end, ﬁrst a model of the score or
certain structural elements and musical elements of thescore is calculated. The score model is then projected onto
performance trajectories (for timing, dynamics etc.) by apredictive model that is usually learned from a large
dataset of expressive performances.
In Widmer, Flossmann, and Grachten (2009) andFlossmann, Grachten, and Widmer (2009) we give a
detailed description of our performance rendering system
YQX. The core of the system is a probabilistic model that
captures dependencies between score and performance
characteristics, and learns to predict expressive timing,
dynamics, and articulation. Given a musical score, thesystem predicts the most likely performance as seen in the
database it was trained on—in our case, the Magaloﬀ
corpus. As the prediction is done note by note for the
melody voice of the piece,10 the system computes acharacterization of all melody notes through a number offeatures that describe some aspects of the local context ofeach melody note. The features—both discrete andcontinuous variables—include among others: the pitchinterval to the next note, the rhythmic and harmonic
context, and the distance to the nearest point of musicalclosure according to an Implication-Realization
analysis (Narmour, 1990) of the melodic content.11 See
Flossmann, Grachten, and Widmer (2009) for further
detail on the score features.
For each melody note three performance characteristics are extracted from the corpus describing the tempo,
dynamics and articulation. The dependencies of scorecharacteristics and performance characteristics are modelled through conditional probability distributions as
depicted in Figure 8: for each conﬁguration of discretefeatures we train a model that relates the continuousfeatures to the observables. Hence, predicting tempo,
dynamics and articulation for a melody note basicallymeans answering the following question: given a speciﬁcFig. 8. YQX: the probabilistic model.
10

We assume that the highest pitch at any given time is the
melody voice of the piece. This very simple heuristic is certainly
not always true, but in the case of Chopin is correct often
enough to be justiﬁable.
11
According to Narmour’s theory, ‘musical closure’ is achieved
when the melodic progression arouses no further expectationsin the listener’s mind. The emerging segmentation of the score iscomparable to a crude phrase structure analysis.

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The Magaloﬀ project
score situation what are the most likely performanceparameters found in the data corpus.
The predicted sequences are then projected onto amechanical MIDI representation of the score in question,
rendering an expressive version of the piece. A crucial
issue is the trade-oﬀ between the speciﬁcity of thedescription of score context on the one hand, and theavailability of training examples on the other. Using
unspeciﬁc score context descriptions it may be impossible
to narrow down an appropriate range of performancefeature values per score context. On the other hand,using too speciﬁc descriptions of score context it is hard
to reliably infer performance feature values, due to thesmall number of instances per score context. By
enhancing the learning algorithm to optimize thepredicted values over the complete piece instead of justchoosing the locally most appropriate, we managed toslightly improve the results (Flossmann, Grachten, &Widmer, 2009).
Judging the expressivity of the generated performancesin terms of how ‘human’ or ‘natural’ they sound is a highly
subjective task. The only scientiﬁc environment forcomparing diﬀerent models according to such criteria isthe annual Performance Rendering Contest RENCON(Hashida, 2008), which oﬀers a platform for presenting
and evaluating, via listener ratings, state-of-the-art
performance modelling systems. In the RENCON 2008,
two pieces speciﬁcally composed for the contest had to berendered autonomously, one piece supposedly being
Mozart-like, the other Chopin-like. Awards were given
for expressivity (RENCON Award, by audience), technical sophistication of the system (RENCON TechnicalAward, by the committee) and for ‘aﬀecting the composer
most’ (RENCON Murao Award, by T. Murao).12
Trained on Magaloﬀ’s Chopin Corpus, YQX won all
three of these.13 See Widmer et al. (2009) for more
information on this, and www.cp.jku.at/projects/yqx
for videos of YQX performing live at the RENCON
contest.

6. Conclusion
The goal of this article was to give the readers a broad
introduction to, and a current status report on, a largescale piano performance research project that is based on
an exceptional corpus of empirical data. Dealing withdata sets of this size raises a number of practical (and insome cases also conceptual) problems, which we tried tobrieﬂy illustrate here. The Magaloﬀ corpus provides us
12

See http://www.renconmusic.org/icmpc2008/autonomous.htm
We only have access to the audience evaluation scores for
the RENCON Award. There, YQX scored a total of 628
points, compared to 515 points scored by the second-ranked
system.

13

375

with unique opportunities for studying a wide range of
piano performance questions in great detail; the speciﬁcstudies presented above are only ﬁrst steps in a much
longer-term research endeavour. While we cannot makethe Magaloﬀ corpus publicly available, due to therestricted, exclusive usage rights associated with it, we
do hope that the experimental results based on it willcontribute new insights to music performance research,
and we hope to be able to at least make available to theresearch community some of the software tools we aredeveloping for this exciting endeavour.

Acknowledgements
We hereby want to express our gratitude to Mme Irène
Magaloﬀ for her generous permission to use this unique
resource for our research. This work is funded by theAustrian National Research Fund FWF via grants
P19349-N15 and Z159 (‘Wittgenstein Award’). The
Austrian Research Institute for Artiﬁcial Intelligenceacknowledges ﬁnancial support from the AustrianFederal Ministries BMWF and BMVIT.

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