# The Batik-plays-Mozart Corpus: Linking
Performance to Score to Musicological Annotations

###### Abstract

We present the Batik-plays-Mozart Corpus, a piano performance dataset
combining professional Mozart piano sonata performances with expert-labelled
scores at a note-precise level. The performances originate from a recording by
Viennese pianist Roland Batik on a computer-monitored Bösendorfer grand piano,
and are available both as MIDI files and audio recordings. They have been
precisely aligned, note by note, with a current standard edition of the
corresponding scores (the New Mozart Edition) in such a way that they can
further be connected to the musicological annotations (harmony, cadences,
phrases) on these scores that were recently published by [1].

The result is a high-quality, high-precision corpus mapping scores and musical
structure annotations to precise note-level professional performance
information. As the first of its kind, it can serve as a valuable resource for
studying various facets of expressive performance and their relationship with
structural aspects.

In the paper, we outline the curation process of the alignment and conduct two
exploratory experiments to demonstrate its usefulness in analyzing expressive
performance.

##  1 Introduction

Music performance is a complex and nuanced activity that involves the
interplay of various expressive features such as timing, dynamics, and
articulation. Expressive performance research in music information retrieval
(MIR) focuses on modeling expressive aspects of music performance by analyzing
how performers use nuances in timing, dynamics, articulation, and other
expressive features to convey their musical intentions, with the aim of
developing computational models that can analyze, recognize, or synthesize
expressive performances [2].

Recent research in this field for Western classical piano has focused on data-
driven approaches both for performance generation [3, 4] and data creation in
the form of large-scale MIDI performance data transcribed from audio
recordings [5, 6]. While such data corpora can be useful for comparative
performance analyses and related tasks (e.g., performer identification,
performance style transfer), they lack the necessary precision and alignment
information (with the underlying musical score) required to precisely map
expressive intentions and parameters to underlying score features.

Compared to these large-scale transcribed MIDI datasets, precise MIDI data (as
recorded on computer controlled grand pianos such as the Yamaha Disklavier or
Boesendorfer SE/Ceus series) along with their corresponding score alignment is
somewhat limited in quantity and size [7, 8, 9]. The performances in such
datasets are typically sourced from advanced piano students or piano
competitions, whereas the digital scores are often obtained from open-source,
user-curated online libraries such as
MuseScore111https://musescore.com/sheetmusic.

Regarding the performance-to-score alignment, one would ideally want to have
note-by-note correspondence information; unfortunately, in the case of the
largest of these datasets [7], score-performance alignments are only given at
a rather coarse level of beats. Score annotations conveying structural
information such as underlying harmony or phrases are even more scarce.

To address these limitations, we introduce the Batik-plays-Mozart
dataset222https://github.com/huispaty/batik_plays_mozart, in which we provide
a set of expert performances of 12 complete Mozart piano sonatas (36 distinct
movements) in MIDI format by concert pianist Roland Batik, precisely aligned,
at a note-by-note-level, to a standard edition (the New Mozart Edition) of the
score, thereby linking the performance information to a previously published
dataset [1] of expert annotations of the scores in terms of harmony, cadence,
and phrase structure. To the best of our knowledge, this is the first corpus
of its kind, combining high quality digital score and structural annotations
with expert performances in recorded MIDI format. We report two preliminary
experiments to demonstrate the benefits of having precise
performance–score–structure annotation alignments.

| Size | Modality | Annotations
---|---|---|---
Dataset | Pieces | Performances | MIDI | Score | Alignment | Other
ASAP[7] | $222$ | $1,068$ | recorded | MusicXML | beat | time and key signature
Vienna4x22[8] | $4$ | $88$ | recorded | MusicXML | note | -
CrestMuse PEDB[9] | $35$ | $411$ | recorded | MusicXML | note | phrase
MazurkaBL[10] | $44$ | $2,000$ | - | MusicXML | beat | dynamics, tempo markings
Batik-plays-Mozart | $36$ | $36$ | recorded | MusicXML | note | phrase, harmony, cadence
Table 1: An overview of publicly available comparable piano performance
datasets for which precise recorded MIDI data, score-performance alignments
and/or musicological annotations are available.

The remainder of this paper is organised as follows: Section 2 presents a list
of comparable expressive performance datasets currently publicly available.
Section 3 describes the data origins, the used data formats, and the curation
process. Section 4 gives an overview of the dataset, and Section 5 describes
two preliminary experiments to demonstrate the benefits of
performance–score–structure annotation alignments. Finally, Section 6
concludes the paper with some remarks for future work.

##  2 Related Work

Several piano performance datasets have been published in the context of
expressive performance analysis and performance rendering. While recently
published datasets are considerably larger than Batik-plays-Mozart, they
provide performance recordings solely in the form of MIDI transcribed from
audio recordings [5, 6] or do not include a high-quality digital score ground
truth [11]. Despite the encouraging results demonstrated by recent
transcription models, they often introduce inaccuracies, such as incorrect
note fragmentation, missed note onsets, and falsely identified notes [12].
Similarly, certain expressive performance aspects such as (micro-)timing and
tempo can only be measured given either a temporal or note-wise score-
performance mapping [2]. Nevertheless, these datasets remain useful for
various related tasks such as symbolic music generation, music transcription
and tagging, or high-level comparative performance analysis.

Table 1 presents an overview of comparable piano performance datasets
currently publicly available, for which precise (recorded) MIDI data, score-
performance alignments and/or musicological annotations are available. Among
these datasets, ASAP [7] stands out as the most extensive one, both in terms
of musical pieces and performer range, with 1,068 performances beat-aligned to
222 scores, each annotated with key and time signature. In comparison to ASAP,
all other publicly accessible datasets are significantly smaller: The Vienna
4x22 corpus [8] contains 22 different performances for excerpts of four
different pieces, each aligned on a note level and provided in
MusicXML333https://www.musicxml.com/, MIDI and audio format. The CrestMuse
PEDB v2.0 [9] provides 35 pieces note-aligned to 411 performances, with scores
provided in MusicXML and MIDI and performances in MIDI and WAV. The dataset
also contains phrase structure annotations, however, merely in the format of
PDF and plain text files, somewhat limiting their (re)usability.

The MazurkaBL dataset [10] consists of a corpus of 44 Chopin Mazurkas with
MusicXML scores that have been beat-aligned to 2000 performances. The
performances themselves are not provided (neither as MIDI nor as audio); only
beat positions and corresponding loudness values are given, along with the
positions of tempo/dynamics markings in the score.

##  3 Curation protocol and file formats

###  3.1 File origins

The MIDI performance files originate from a performance of twelve Mozart piano
sonatas by Viennese concert pianist Roland Batik on a computer-controlled
Bösendorfer SE290 grand piano, the predecessor of the CEUS model. The
Bösendorfer SE series measures each individual keystroke and pedal movement
precisely, with onset and offset times being captured at a time resolution of
1.25ms. Hammer velocity values are captured in a proprietary file format, and
converted and mapped to the 128 dynamics MIDI values (see [13] for conversion
details). The audio recordings corresponding to those MIDI files can be
purchased commercially444 https://www.gramola.at/products/9003643987012.

These MIDI performance data were originally aligned manually, on a note-to-
note level, to a symbolic encoding of the score produced by our team [14, 15].
In order to make it possible to link the performance data in an unequivocal
way to the musicological score annotations provided in the Annotated Mozart
Sonatas dataset by Hentschel et al. [1], we decided to replace our score
encoding in the alignments entirely by the score notes as given in the their
dataset, which link to their annotations directly via absolute temporal score
position. The scores in the Annotated Mozart Sonatas dataset conform to the
New Mozart Edition555https://dme.mozarteum.at/DME/nma/start.php?l=2 and are
given in MuseScore format, with the harmony, phrase and cadence label
annotations provided in tabular format, as tab-separated values (TSV) files.

###  3.2 The match alignment format

We provide the alignment between the above-mentioned score and performance
files in the match file format [16], a file format for symbolic music
alignment in a human-understandable textual form. It is structured
sequentially, and the alignment information is given at the level of
individual notes.

The encoded alignment is complete in the sense that all performance and all
score notes are captured. Each performance and each score note is represented
with their respective note ID, and their respective alignment can be recorded
with one out of three potential tuples: 1. A match between score note and
performance note, i.e., (score_id, performance_id), 2. a deleted score note
(score_id, ) which represents a score note omitted in the performance, or 3.
an inserted performance note ( , performance_id), which marks a performed note
for which there is no corresponding score note.

Following this alignment encoding, each line in a match file corresponds to
either a match, a deletion or an insertion. Additional lines express (sustain
or soft) pedal information, or encode meta information about the musical piece
and performer. While the performance part in match corresponds to a lossless
encoding of a corresponding performance in MIDI format, the score part
captures essential information including onset, offset and duration in beats,
and pitch, pitch spelling, and octave information for each score note.

###  3.3 Curation protocol

To create note-level score-to-performance alignments, encoded in the match
file format, between the performance MIDI data by pianist Roland Batik and
scores and musicological annotations by Hentschel et al. [1], we follow the
workflow as outlined below (see Fig. 1):

Figure 1: Visual illustration of the alignment process. Each step in the
alignment process is numbered according to the textual description in Section
3.3. Steps marked * indicate manual correction / post-processing. Elements
highlighted in green are combined in the new alignment match files.

  1. 1.

Retrieve information from old alignment. Given an old alignment file, we use
partitura [17] to retrieve a score and performance representation which we
parse into score and performance note arrays, sna_o and pna_o, to sequentially
capture each (notated and performed) note with a unique note ID. In addition
we retrieve a score-to-performance alignment, align_o, in the encoding format
explained above (i.e., a list of note ID tuples expressing either a match,
deletion or insertion).

  2. 2.

Retrieve score note array from MusicXML. In the next step, we convert the
annotated MuseScore format scores provided by Hentschel et al. [1] to
MusicXML, assign unique note IDs to each note, and convert this score
representation into a second score note array (sna_s).

  3. 3.

Unfold score note array. We update the score note array obtained from
MusicXML, sna_s, by unfolding it in accordance to the repetition structure
found in the performance note array, pna_o. 666To reflect the same note
occuring in a repeated segment, a suffix is added to the ID to reflect the
number of occurrence, i.e. for a note with ID n14, the repeat structure
unfolding is expressed as n14-1 for the first, and n14-2 for the second
occurrence, respectively.

  4. 4.

Create score-score alignment. In this step, we create a score-to-score
alignment (align_s) by matching each note in the two score note arrays sna_o
and sna_s using its pitch, onset and duration information in beats. Any notes
in sna_o and sna_s not matched automatically need to be aligned manually.
Missed alignments at this stage can occur due to:

     * •

Score mistakes. These reflect mistakes in the score (e.g., a missing note,
incorrect pitch, octave, missing modifier, missing repetition or ending
markings) and require a manual correction of the score file.

     * •

Differing score versions. For certain sonata movements, the notated score
provides an alternative score version reflecting the first edition
(“Erstdruck”) for certain segments of a piece, expressing the composer’s
impromptu ornamentation.777https://www.henle.de/en/music-column/mozart-piano-
sonatas/ For the current dataset, such ornamented versions exist in K.284iii,
K.332ii, K.457iii.

     * •

Double-voiced score notes. These occur frequently in notated music, and
describe a score note that is notated doubly in two different voices but
corresponds to one performed note.

     * •

Grace notes. Grace notes in notated music can occur in multiple forms to
reflect different types of ornaments such as trills, acciaccature, mordents,
turns etc. Depending on the ornament type and the underlying score encoding
format, this may result in several notes occurring at the same (notated) onset
(and hence with zero duration) to ensure a regular measure according to the
time signature of that piece. Without onset and duration information, these
notes must then be manually aligned to their corresponding performed notes.

     * •

Cadenza and ad libitum measures. Both cadenza measures and those marked ad
libitum correspond to irregular measures, that is, measures that contain more
beats than indicated in the time signature (see Fig. 2). Digitally encoded,
the notes in such measures are commonly notated without duration to allow for
error-free parsing, and thus share the same beat onset and need to be aligned
manually.

Figure 2: An example of a cadenza within a piano sonata starting in measure
198 in KV333, 3rd movement.

  5. 5.

Update score-performance alignment. Here we update the score note IDs in the
old alignment (align_o) according to the score-score alignment (align_s) to
create new score-performance alignments, align_n. For each alignment in
align_o, we then need to ensure the validity of the original alignment type
(match, insertion or deletion). In particular, for notes in the original score
note array (sna_o) that could not be aligned to notes in the MusicXML-based
score note array (sna_s), we consider two cases:

     * •

If the note in sna_o corresponds to type ‘match’ in align_o, the alignment
type for the formerly matched performance note is changed accordingly into an
insertion.

     * •

If the note in sna_o corresponds to type ‘deletion’ in align_o (i.e., a score
note that was not performed), it is is discarded in align_n.

Notes in sna_s that could not be aligned with notes in sna_o, on the other
hand, are recorded as type ‘deletion’ in align_n.

  6. 6.

Create match files. Using the updated performance-to-score alignment align_n,
we create new match files, and manually add attributional information (e.g.,
‘diff_score_version’, ‘voice_overlap’) to score notes to reflect edge cases
described in step 4.

##  4 Dataset Overview

Sonata | Performed Notes | Duration (min) | Match Notes | % | Insertion Notes | % | Deletion Notes | %
---|---|---|---|---|---|---|---|---
KV279 | $7,789$ | $16.21$ | $7,385$ | $94.087$ | $404$ | $5.780$ | $11$ | $0.130$
KV280 | $6,277$ | $14.69$ | $6,070$ | $95.793$ | $207$ | $3.983$ | $13$ | $0.223$
KV281 | $7,030$ | $14.43$ | $6,396$ | $90.450$ | $634$ | $9.393$ | $11$ | $0.160$
KV282 | $5,761$ | $14.77$ | $5,552$ | $96.197$ | $209$ | $3.467$ | $20$ | $0.337$
KV283 | $8,231$ | $17.39$ | $7,915$ | $95.657$ | $316$ | $4.233$ | $9$ | $0.107$
KV284 | $13,386$ | $25.92$ | $12,691$ | $93.763$ | $695$ | $6.033$ | $27$ | $0.203$
KV330 | $7,869$ | $18.47$ | $7,589$ | $96.857$ | $280$ | $3.047$ | $7$ | $0.100$
KV331 | $11,760$ | $22.64$ | $11,595$ | $98.283$ | $165$ | $1.370$ | $45$ | $0.347$
KV332 | $9,013$ | $17.84$ | $8,660$ | $93.417$ | $353$ | $6.210$ | $24$ | $0.373$
KV333 | $9,137$ | $20.40$ | $8,827$ | $96.723$ | $310$ | $3.120$ | $16$ | $0.157$
KV457 | $7,290$ | $18.24$ | $7,022$ | $96.043$ | $268$ | $3.843$ | $9$ | $0.110$
KV533 | $8,878$ | $22.12$ | $8,616$ | $97.027$ | $262$ | $2.837$ | $15$ | $0.137$
Total | $102,421$ | $223.12$ | $98,318$ | $95.358$ | $4,103$ | $4.443$ | $207$ | $0.199$
Table 2: List of sonatas in the Batik-plays-Mozart dataset. The bottom row
represents the sum in all columns except for those expressing percentages, for
which the mean is shown.

The Batik-plays-Mozart dataset contains performances by pianist Roland Batik
of twelve Mozart sonatas (see Table 2 for the list of sonatas), corresponding
to approx. 102,400 played notes and 223 minutes of music, for which the
performances are provided in MIDI, musical scores in MusicXML, and the
alignment in match file format. Approximately 98,300 (95.36%) of all performed
notes are aligned with a corresponding score note, the remaining 4,100 (4.44%)
represent insertions (reflecting mostly ornaments). Roughly 200 score notes
have been omitted in the performances.

For each performance, we also provide the performance note arrays, which
capture each played note with its note ID along with onset and duration
information in seconds and MIDI ticks, as well as velocity and pitch
information. Likewise, the dataset includes the score note array (unfolded
according to the repeats as played by the pianist and reflected in the
alignment), which captures each score note with its (MusicXML) note ID
(including repeat suffices, where applicable), onset and duration information
in terms of beats (reflecting the time signature), and quarter notes
(reflecting a “normalized” score time unit), and pitch and voice information.

We link our aligned score note arrays to the musicological annotations in [1]
via their temporal position in the following way: In the second version888
https://github.com/DCMLab/mozart_piano_sonatas of the dataset, each annotation
label for harmonies, cadences, and phrases is unequivocally referenced to a
temporal score position represented in terms of quarterbeats and measure
number, where the first expresses the distance of the label from the beginning
of the piece in quarter note units. We leverage these two temporal parameters
to link each note-aligned score note array by first reducing it to its
shortest form (without any unfolded repeats), aligning it temporally with the
musicological annotations, and eventually unfolding it according to the
performed repetition structure.

##  5 Dataset Demonstrations

This section presents two simple examples of the kinds of studies that are
made possible by our dataset. The first is motivated by a directly related
study in the Annotated Mozart Sonata corpus paper [1]; the second shows how
precise performance alignments permit more detailed investigations relating to
cadences and their performance.

###  5.1 Global tempo and harmonic density

In a first study, we replicate the second experiment in Hentschel et al. [1],
aimed at investigating the relationship between tempo and harmonic change
rate. The basic question asked in [1] was whether the rate at which the
harmony changes in a piece is correlated with the piece’s typical performance
tempo. Their study involved determining the average (median) performance
duration of each sonata movement from 6 complete commercial sonata recordings,
and correlating harmonic label density (rate of harmonic labels in their
annotations, per performance time unit) with average overall performance tempo
(number of quarter notes per performance time unit). We repeat the same
experiment with our pianist’s performances and our alignment files instead of
6 pianists’ audio recordings.

We apply the same procedure as in [1], unfolding the score according to the
repeat structure of the piece in order to calculate the actual piece length
(in terms of quarter notes). The only difference is that we do this according
to the repeats actually performed by the pianist (which are expressed in our
match files, thus omitting the need for a dedicated “unfolding" step), whereas
[1] seem to have assumed that all repeats were played by all pianists.

Comparing our results (Fig. 3) to Fig. 10 in [1], we see a similar general
trend, in the form of a roughly linear increase in harmonic label density with
performed tempo (slope = .43, r= .75, compared to .48 and .80, respectively,
in [1]).999Note that we have a somewhat smaller set of points, because we only
have 12 of the 18 sonatas in our dataset. However, we also immediately see a
marked difference in the performance tempo distribution: in [1], Fig. 10,
there is a relatively large cloud of points (sonata movements) with
conspicuously high tempos of 180–200 (quarters per minute), which does not
appear in our plot, and which we believe may point to a systematic problem in
their way of estimating playing tempo: assuming that all notated repeats are
played out by the performers leads them to overestimate the tempo in all cases
where some or a majority skipped some repeats.101010 Of course, the authors
explicitly acknowledge the problem: “Also, some of the initial assumptions
might have to be revisited. For example, the extreme outlier suggesting a
tempo of 239 quarter notes per minute is due to the fact that for this
particular piece – the first movement of K. 533/494 – there seems to be a
convention among pianists to repeat the first part of the piece, but not the
second (as the score would suggest), which of course reduces the performance
duration.” [1] (p.76), but a comparison with our distribution implies it might
be more severe than expected.

We thus see an immediate advantage of our more precise performance-aligned
corpus: the match files naturally give correct tempo and score duration
information, being based as they are on score-performance alignments that
reflect the actual repeat structure played by our performer. Still, we can say
that our results support and confirm the overall hypotheses proposed there,
showing a more or less linear relationship between harmonic label density and
global performance tempo.

Figure 3: Correlation between global tempo (as measured in quarter notes per
minute) and harmony label density

###  5.2 Performance of different cadence types

Figure 4:  Comparison of local timing strategies one quarter note before and
after authentic and half cadence labels, over different tempo classes (in
increasing tempo from top to bottom), for cadences falling on a downbeat
(left) or weak beat (right). Colour identifies cadence type, line style
notated tempo class.

Our data permits much more detailed investigations into relationships between
structural aspects of a piece, and how these are translated into performance
decisions by a pianist. As a simple example, we investigate variations in
local tempo before various types of cadences. Specifically, we compare the
local tempo prior to a cadence annotation across different tempo classes for
authentic (perfect and imperfect, i.e., PAC and IAC) and half cadences (HC),
and differentiate between the cases when a cadence falls on either a downbeat
or a weak beat. The hypothesis to be tested here is that a performer will tend
to shape cadences differently, in terms of tempo, depending on their type and
degree of ‘finality’.

To compute the local tempo curves, we consider a uniform window spanning one
quarter note each preceding and following a cadence label.111111 In the
Annotated Mozart Sonatas Corpus [1], cadence labels are placed at the onset of
the final target harmony (e.g., I/i for authentic cadences). For each score-
note-aligned performed note in that window, we define the local tempo via the
beat period (BP), which we calculate as the ratio of the inter-onset-interval
(IOI) between the current performed onset and the subsequent one, and the IOI
between the current notated onset and subsequent one. We exclude grace notes
and their corresponding performed notes from this calculation in order to
remove outliers.

Next, we perform time-wise interpolation on these tempo curves to obtain beat
period values at eighth note intervals within the window. Given that we are
most interested in the local timing strategy immediately before a label (that
is, an eighth note before the label position), we discard those curves where
that particular time point is interpolated. Following this procedure, we
obtain a total of 3,540 local tempo values (corresponding to 708 curves), of
which 251 (7.09%) values are interpolated.

Figure 4 shows the mean of local tempo curves across different tempo classes,
for cadence labels annotated on a downbeat (left) and on a weak beat (right),
respectively. For both authentic and half cadence types, the differences in
local tempo diminish with increasing global tempo for both downbeat and weak
beat cadences. Likewise, the tempo profiles tend to flatten out with
increasing global tempo, suggesting that the pianist takes more liberty, in
terms of expressive timing, in slow pieces. For this reason, we focus our
analysis on the adagio tempo class, the slowest tempo (the solid line plots in
Fig. 4) .

The influence of the beat level on the local tempo for half cadences seems to
be negligible, with the local beat period decreasing slightly prior to the
cadence (causing an increase in local tempo, i.e. an accelerando), regardless
of whether it falls on a downbeat or weak beat. For authentic cadences, we can
see a substantial difference in expressive tempo depending on whether or not
the label falls on a downbeat: for authentic cadences falling on a downbeat,
the mean tempo curve for the adagio tempo class corresponds mostly to what one
would expect (i.e., a very clear ritardando in preparation of the cadence)
based on the underlying harmonies and their notion of tension and release.
Interestingly, this ritard seems to continue somewhat after the resolution
into the tonic, suggesting a lengthening of the tonic arrival. For weak-beat
authentic cadences, a similar significant preparation or anticipation is
largely missing.

##  6 Conclusion and Future Work

We have presented Batik-plays-Mozart, a piano performance dataset linking
professional Mozart piano sonata performances to expert-labelled musical
scores, at the level of notes. The resulting dataset is the first of its kind
to combine professional performances in precise, recorded MIDI with curated
musical scores and expert musicological and structural annotations [1] at this
level of detail.

We presented two preliminary experiments, intended to demonstrate the benefits
of having such precise, note-aligned performance–score–structure annotation
data for studying expressive features and their relation to the underlying
musical structure.

Our plan for future work includes the transcription of the remaining six
sonatas of the Mozart piano sonatas corpus from audio recordings by the same
pianist, and their subsequent alignment to the musical scores using state-of-
the-art transcription and alignment models. By doing so, we hope to advance
our understanding of the differences between transcribed and recorded MIDI,
and to evaluate the potential benefits of incorporating an alignment step to
improve the quality of transcription.

##  7 Acknowledgments

We wish to express our gratitude to pianist Roland Batik for his gracious
permission to publish the detailed measurements of his performances. We also
want to thank the authors of the Annotated Mozart Sonatas Corpus for their
tremendous efforts, and for permitting us to link our data to theirs. This
work receives funding from the European Research Council (ERC), under the
European Union’s Horizon 2020 research and innovation programme, grant
agreement No. 101019375 (Whither Music?). The LIT AI Lab is supported by the
Federal State of Upper Austria.

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