CHORD JAZZIFICATION:LEARNING JAZZ INTERPRETATIONS OF CHORD SYMBOLS
Tsung-Ping Chen1
Satoru Fukayama2
Masataka Goto2
Li Su11
Institute of Information Science, Academia Sinica, Taiwan
2
National Institute of Advanced Industrial Science and Technology (AIST), Japan{tearfulcanon, lisu}@iis.sinica.edu.tw{s.fukayama, m.goto}@aist.go.jp

ABSTRACT
Chord symbols, typically notating the root note and thechord quality, are extensively used yet oversimplified representation of tonal harmony and chord progressions in
popular music. In spite of its convenience, the chord symbol notation only provides basic information about thechordal configuration, and leaves much room for interpretation. With such limitations, an algorithm generating
merely chord symbols is usually insufficient for a wide
range of music genres such as jazz. To solve this problem,
we propose chord jazzification, a process to generate realistic chord configurations in jazz style. With deep learning
approaches, we decompose chord jazzification into coloring and voicing. Coloring concerns the choice of color
tones, while voicing concerns the configurations of chords.
We also create a new dataset featuring interpretations ofchord symbols in pop-jazz compositions. By conducting
experiments on the new dataset, we show that 1) the twostage process outperforms an end-to-end generation approach in modeling chord configurations, and 2) attentionbased models are better at capturing the structure of chord
sequences in comparison with recurrent neural networks.
1. INTRODUCTION
Harmony and chords are the central topic in the study of
tonal music. To facilitate the study, researchers in the fields
of music information retrieval (MIR) and computationalmusicology have developed various techniques, such as automatic chord recognition [1–5], chord similarity and tonal
distance [6–9], harmonic analysis [10–13], and chord generation [14–16].
Most of these aforementioned techniques process thechord data using chord symbol representation, i.e., a symbolic notation system indicating the root note, quality, andother additional information of the chord. For example,
the chord symbol C:7 stands for the C dominant seventh
chord in root position, whose theoretical configuration isc Tsung-Ping Chen, Satoru Fukayama, Masataka Goto, Li
Su. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Tsung-Ping Chen, Satoru Fukayama,
Masataka Goto, Li Su, “Chord Jazzification: Learning Jazz Interpretations of Chord Symbols”, in Proc. of the 21st Int. Society for Music
Information Retrieval Conf., Montréal, Canada, 2020.

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Figure 1: Three configurations of the C dominant seventh
chord. In music theory, the C dominant seventh chord inroot position is configured as (a). (b) and (c) are two alterations of the chord.

depicted in Figure 1a. This symbol, however, does not explicitly point out the actual configuration, e.g., every specific note performed by a musician. Hence, the notation
system is limited in describing the nuances in real-world
performance. Learning to interpret the chord symbols ischallenging in two aspects. First, expert musicians often
color a chord by adding notes to, or omitting notes fromthe chord according to the musical context, although thechord symbol itself does not specify such alterations. Second, chords composed of the same set of pitch classes canbe voiced differently by spacing and doubling the chordtones. Take the comping technique in jazz music as an
instance. 1 Instead of sticking to the typical configuration of a chord, a jazz pianist may play the C dominantseventh chord with the configuration shown in Figure 1b,
in which the 5th of the root, G4, is omitted, and the 9thof the root, D5, is added; while another jazz pianist may
arrange these notes in a totally different way as demonstrated in Figure 1c, where Bb is doubled and D is spaceda register lower. In fact, the way how musicians interpretchord symbols in a given context involves not only theirthorough understanding of various musical styles, but also
their personal tastes. Therefore, to generate the realizationof chord symbols through MIR approaches is a challenging yet valuable task, despite this topic is rarely discussed
possibly because of the lack of data.
In this paper, we propose chord jazzification, a process
to realize chord symbols with jazz harmony through deeplearning approaches. Based on the two aforementioned aspects of interpreting the chord symbols, the chord jazz1 Comping means accompanying or complementing a soloist by playing the chords to fill the harmonic and rhythmical vacancies in the music.

Proceedings of the 21st ISMIR Conference, Montréal, Canada, October 11-16, 2020

Figure 2: Annotations of the proposed dataset. Chord Symbol specifies the root and the quality of each chord (a bass noteis explicitly notated with a slash when it is not the root note), e.g., Db:M/F stands for a Db major triad with the bass note
F. Time indicates the onset and the duration (measured in beats) of each chord. Voicing represents the configuration of eachchord with a set of scientific pitch notations. Coloring indicates the chord degrees which appear in the voicing but are notspecified by the chord symbol, and vice versa, e.g., (o1,9) indicates that the root is omitted and the 9th is added. Notethat 7 is explicitly qualified by {M, m, d} (major, minor, diminished) to disambiguate its interval. Roman Numeral Analysis
denotes the scale degree on which a chord is built, as well as the quality and the inversion information, e.g., V7 stands fora dominant seventh chord in root position. Structure includes the annotations of phrase, measure and metrical position. To
specify the phrase a chord belongs to, each chord is labeled with a letter plus a number in a way similar to musical form
analysis. Metrical position shows the position of a chord with respect to the metric grid (starting with 0).
ification task is formulated as two subtasks, namely thechord coloring and the chord voicing. The chord coloringtask decides which pitch classes are to be played for elaborating a chord symbol, while the chord voicing task deals
with the spacing and the doubling of the pitch classes to beplayed. Although the jazzification of chord progressions is
not limited to coloring and voicing, we focus on the two
aspects for the primary study. To facilitate the research on
chord jazzification, we also compile a new dataset consisting of chord symbols and corresponding chord configurations in pop-jazz compositions. In comparison with otherjazz-related datasets, such as the Charlie Parker’s Omnibook data [17], the Jazz Audio-Aligned Harmony (JAAH)
dataset [18], the JazzCorpus [19], and the Weimar Jazz
Database [20], our dataset includes more detailed information of chords, especially the chordal configuration. With
the newly compiled dataset, we conduct several experiments to verify the two-stage framework for chord jazzification. Experiment results indicate that it is effective todecompose chord jazzification into coloring and voicing,rather than to adopt an end-to-end approach.
The current work is different from the accompanimentor harmonization tasks [21,22], in terms of that such works
require melodies as prior knowledge and regard chords asappendages to melodies. Besides, our work concerns boththe voicings of a chord sequence and the interpretation ofeach single chord, thus is distinct from the voice leading
generation task [23, 24]. The jazzification of a chord progression is part of the music composition process to expand

a tonal structure, i.e., the prolongation described in Heinrich Schenker’s music theory [25]. Chord jazzification canadvance other MIR-related tasks such as style analysis andchord generation [26–29]. We hope that our work can draw
more attention to the musical knowledge regarding the implicit relations between the notated chord symbols and theactual harmony being performed.
In summary, our contribution is threefold. First, weaddress the issue of interpreting chord symbols by chordjazzification. Second, we compile a new dataset for thegeneration of jazzy harmony and for the study of chordembellishments. Finally, a deep learning framework is proposed to generate jazz-style chord progressions. In the following, we will first present the new dataset (Section 2),
and then formulate the framework of chord jazzification(Section 3); based on the framework, several experiments
are introduced thereon (Section 4).
2. THE CHORD JAZZIFICATION DATASETThe corpus is composed of 50 musical pieces selected frompublished Japanese pop-jazz piano solos, in which chordsymbols are explicitly specified. 2 To obtain the corresponding voicing of each chord symbol, we manually perform harmonic reduction for each piece. Concretely, notes
within the region of a chord symbol are selected to build
the configuration of the chord symbol. With the chord
2 The
dataset is available at https://github.com/
Tsung-Ping/Chord-Jazzification.

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3. CHORD JAZZIFICATION
The goal of chord jazzification is to endow plain chords(e.g., a sequence of triads) with jazz harmony. We tackle
the chord jazzification task through two successive steps,that is, coloring and voicing. The coloring part functions
as an intermediate state which specifies chords in terms ofpitch classes, and the voicing part assigns pitch heights tothe specified pitch classes.
3.1 Coloring

Figure 3: Chord qualities and chord colorings in the proposed dataset (the long tail of the coloring distribution isleft out). Chord degrees of compound intervals are mergedwith simple ones in the coloring figure for simplicity.

symbols and the transcribed voicings, coloring and harmonic information are annotated. Specifically, there are six
types of annotations in the dataset: Chord Symbol, Time,
Voicing, Coloring, Roman Numeral Analysis [10, 30], andStructure. Figure 2 gives an example.
A brief introduction of the chord qualities and colorings
used in the corpus is illustrated in Figure 3. Not surprisingly, the three seventh chords, major seventh (M7), minor
seventh (m7), and dominant seventh (7), account for morethan half of the chords, as jazz harmony is notable for theuse of seventh chords. On the other hand, characteristiccolorings of pop-jazz music can also be found in many
chords, such as adding a major second or a compound major second (2) and omitting the perfect fifth (o5).
In summary, 796 musical phrases amounting to 6700
chord labels are included in the dataset. These annotations provide information concerning the relationship between the symbolic notation and the actual configurationof chords in human compositions. It therefore has the potential to be applied to many MIR-related research topics, such as corpus-based study of tonal harmony in music practice, generation of colorful chord progressions, andcomputer-aided composition, to name but a few. It has tobe acknowledged that the dataset has some limitations indescribing an actual interpretation of chord symbols, in thesense that the transcription of chords eliminates the harmonic and rhythmical variances within the region of eachchord symbol. Nevertheless, the dataset can be a starting
point for performers and composers to learn to elaborate
and develop a tonal structure through the chord jazzification process, which is relatively easier compared to learning the elaboration directly from a complete musical piece.
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In this paper, 48 triads in root position ({major, minor, augmented, diminished} by 12 semitones) are considered forcoloring. We define the chord coloring task as predicting
the bass note and the pitch classes to render each triad in agiven sequence.
Formally, the input of the coloring task is a triad sequence {xi }Ti=1 and a duration sequence {di }Ti=1 , where
i denotes time steps, T is the length of the sequence,xi ∈ R12 is a chroma representation of the ith triad,
and di is the duration of xi . The coloring task predicts the bass sequence {bci }Ti=1 and the pitch class sequence {pci }Ti=1 for the input sequence, where c stands forcoloring, bci ∈ R12 is a softmax-activated chroma vector indicating the 12 pitch classes’ probabilities to be thebass of colored xi , and pci ∈ R12 is a sigmoid-activatedchroma vector indicating the 12 pitch classes’ probabilities
to be the constituent notes (except the bass) of colored xi .
For example, bci = [0.8, 0, 0, 0, 0.2, 0, 0, 0, 0, 0, 0, 0] indicates the pitch classes C and E respectively have a probability of 80% and 20% to be the bass note, and pci =[0, 0.9, 0, 0, 0.9, 0, 0, 0, 0, 0, 0.9, 0] suggests that there is a
90% chance that Db , E, and Bb are activated to render xi .
We employ a basic sequential learning architecture forthe coloring task. As shown in Figure 4a, the architecture
is composed of three layers: 1) an input embedding layer,2) a sequential modeling layer, and 3) an output layer.
Specifically, the three layers are formulated as follows:c

eci = We (di xi ), (Input Embedding)
hci = f c (eci | ec1:T ), (Sequential Modeling)c

pci = sigmoid(Wp hci ), (Output)(1)c

bci = softmax(Wb hci ),cccwhere We ∈ Rd×12 , Wp ∈ R12×d , and Wb ∈ R12×d
are learnable parameters, f c : Rd → Rd denotes a trainable neural network, and d is a hyperparameter indicating
the dimensions of the embedding space. Two candidate
networks are employed for the sequential modeling layer:• Bi-directional Recurrent Neural Network with Long
Short-Term Memory (BLSTM):→−←−
hci = h ci ⊕ h ci ,→−ch i = LSTM(eci | ec1:i−1 ),←−h ci = LSTM(eci | eci+1:T ),where ⊕ denotes vector concatenation.

(2)

Proceedings of the 21st ISMIR Conference, Montréal, Canada, October 11-16, 2020

(b)

(a)

(c)

Figure 4: (a) The coloring model. (b) The voicing model. (c) The two-stage chord jazzification model.
• Multihead Self-attention Network (MHSA):
c(l)

hi

= Wouter σ(Winner ui + b1 ) + b2 ,c(l−1)

ui = Wu (u0i1 ⊕ · · · ⊕ u0iJ ) + hi
!
>
K
q
ij
j
√
u0ij = Vj softmax
,
d

,

(3)

pvi ∈ R12 is a multi-hot chroma vector representing thepitch classes of the ith chord except the bass note, andvi ∈ R88 is a voicing vector indicating the 88 tones’ probabilities to be played on the piano.
Similar to the coloring task, we employ a 3-layer architecture for the voicing task, as shown in Figure 4b. The
three layers are formulated as follows:c(l−1)
qij = WjQ hi,c(l−1)

Kj = WjK [h1vevi = We (di (pvi ⊕ bvi )), (Input Embedding)c(l−1), · · · , hT

hvi = f v (evi | ev1:T ), (Sequential Modeling)],c(l−1)c(l−1)
Vj = WjV [h1, · · · , hT],

vi = sigmoid(Wvhvi ),(5)(Output)v

where l denotes the iteration step, and the initialc(0)value hi
= eci ; σ represents the ReLU activation function; J is the number of heads; Wjouter ∈Rd×4d , Wjinner ∈ R4d×d , Wu ∈ Rd×d , andd
WjQ , WjK , WjV ∈ R J ×d are learnable parameters.
This network is equivalent to the encoder of theTransformer [31], while we leave out layer normalization and position encoding terms for simplicity.
In this paper, we set d = 512, l = 2, and J = 8.

where We ∈ Rd×24 and Wv ∈ R88×d are learnable parameters, and f v : Rd → Rd is a neural network. Likewise, the BLSTM and the MHSA networks are two options
for the sequential modeling layer.
Let vi∗ ∈ R88 denote the target voicing of the ith chord;
we define the loss as:

The binary cross entropy (BCE) and the categoricalcross entropy (CCE) are used to calculate the losses. Letccpic∗ and bc∗
i denote the ground truths of pi and bi ; thectotal loss of the coloring model L is defined as:

As the voicing task is to arrange the constituent notes ofchords on an 88-key piano based on the given basses andthe sets of pitch classes, the outcome of vi∗ can be known
to a certain degree. More precisely, a note in vi∗ can be
activated only if its pitch class is activated in bvi or pvi .
With this consideration, we design corresponding masks88
to modify the loss computation. Let bv0
and pv0i ∈ R
i ∈R88 be the extensions of bvi and pvi to all octaves of the
piano. Then, the loss constrained by the masks becomes:

Lc =

T
X

c
c∗
c
[BCE(pc∗
i , pi ) + CCE(bi , bi )] .

(4)

i=1

3.2 Voicing
We define chord voicing as a task which predicts the voicings of a chord sequence. Formally, given a chord sequence of T time steps in terms of their basses {bvi }Ti=1 ,constituent pitch classes {pvi }Ti=1 , and durations {di }Ti=1 ,the task predicts the voicings {vi }Ti=1 for the chord sequence, where v stands for voicing, bvi ∈ R12 is a onehot chroma vector indicating the bass of the ith chord,

Lv =

T
X

BCE(vi∗ , vi ).

(6)

i=1

Lv0 =

T
X

BCE(vi∗ , mii=1v0
mi = bv0i ∨ pi ,

vi ),

(7)

(Mask)

where stands for the Hadamard product, and ∨ denotes
the logical OR operator.
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3.3 Two-stage Chord Jazzification

BLSTM
MHSA

Bass
81.87
80.78

Coloring
Pitch Classes
76.52
77.02

BLSTM w/o E
BLSTM w/o M
End-to-End

77.18
-

73.33
-

Model

We stack the chord voicing model on the top of the chordcoloring model by setting bi = bvi = onehot(arg max bci )and pi = pvi = round(pci ), as illustrated in Figure 4c. In
other words, the outputs of the coloring model are first converted into binary vectors, and then taken as inputs by thevoicing model. Such an integrated model jazzifies chord
progressions in two stages: first, for a given sequence oftriads {xi }Ti=1 and the corresponding durations {di }Ti=1 ,the coloring model generates a colored sequence represented by {bi }Ti=1 and {pi }Ti=1 ; based on the colored sequence, the voicing model subsequently generates a voiced
chord progression {v}Ti=1 .

Voicing
63.64
64.86
60.12
60.98
37.87

Table 1: Results of the coloring and the voicing tasks. The
lower part shows the ablation tests without the embedding
layer (w/o E) and without masks (w/o M), as well as theresult using end-to-end training. All the values indicate
the average F1 scores (%) over 4 validations.

4. EXPERIMENTS
4.1 Chord Jazzification with Supervised Learning
With the formulations of chord jazzification as multi-classclassification (for bci ) and multi-label classification (for pci
and vi ) problems, we train the coloring and voicing models using the new dataset, and perform 4-fold cross validation. For the coloring task, the input triad sequences andthe input duration sequences are respectively derived fromthe Chord Symbol and the Time annotations of the dataset,
while the ground truths of the bass sequences and the pitchclass sequences are obtained from the Voicing labels. As
for the voicing task, the duration sequences and the groundtruth labels of the coloring task are taken as the inputs,
while the Voicing labels are used as the ground truths ofthe output sequences. We augment the training set throughtransposing the data from 4 semitones down to 5 semitonesup (within the valid range of the piano), leading to 10 times
the training data. As a result, there are 5970 and 199 sequences for training and testing respectively.
Evaluation results are shown in Table 1. For both thecoloring and voicing tasks, the employment of either the
BLSTM or the MHSA as the sequential modeling layers yields comparable performance to the other, while the
MHSA appears to surpass the BLSTM in cases of multilabel classification, i.e., the predictions of pitch classes andvoicing. When the input embedding layers in the two subtasks are removed, all the performances decrease by from
3.19% to 4.69%. This indicates that the transformation todense vectors benefits the learning process when the input
data is sparsely represented. Moreover, the introduction of
the input-related masks to the loss calculation in Eqn (7)also improves the modeling of voicing; precisely, the F1score increases 2.66% if the masks are utilized. It is worth
noting that the amount of training data is quite limited, andtherefore the performance seems to be satisfactory in thecurrent experimental setting.
4.2 End-to-end Chord Jazzification
To motivate the decomposition of chord jazzification into
2 stages, we train a chord jazzification model in an end-toend manner for comparison. Technically, we replace theoutput module of the coloring model with that of the voicing model; and we employ a 2-layer BLSTM, rather than364

a 1-layer BLSTM as defined in Eqn (2), for the sequential
modeling layer in order to make the number of parameters comparable to the two-stage chord jazzification model.
The evaluation result is shown in Table 1.
In this end-to-end architecture, the performance drops
substantially to nearly half of the value. This result conversely validates the two-stage approach. Given the factthat the prediction of polyphony is often challenging, itturns out to be beneficial to generate an intermediate stage,that is, the chroma representations with respect to coloring,
before the overall jazzification of chords.
4.3 Consistency of Chord Jazzification
Chord progressions often have a repetitive structure, therefore it is important for a model to preserve this property
and generate chord sequences of self-consistency. To measure the consistency of a model’s generations, we compute the self-similarity matrices (SSMs) of each generated
voicing sequence and corresponding label sequence, andthen calculate the difference between each generation-label
SSM pair. Let V = [v1 , · · · , vT ] denote the normal∗
ized voicing sequence generated by a model, and V =[v∗1 , · · · , v∗T ] denote the normalized label sequence, whereround(vi )vi = kround(vis a binarized and normalized voicing, andi )kv∗v∗i = kvi∗ k is a normalized target voicing; we define theiconsistency score (CS) of a generated sequence as follows:
CS = 1 − reduce_mean(∆SSM),∆SSM = |SSMpred − SSMlabel |,(8)>

SSMpred = V V,
SSMlabel = V

∗>

∗

V .

The more similar the structures of the two sequences are,the higher the CS score is.
Table 2 shows the average consistency score over 4
cross-validation folds. To provide a benchmark for theconsistency measure, we also show the CS score computedfrom label sequences and randomly-generated sequences(denoted as RANDOM). Both the two models get higherscores than the random condition, indicating that they learnsome structural information. Besides, the MHSA outperforms the BLSTM due to an essential difference between them: the BLSTM processes each time step of a

Proceedings of the 21st ISMIR Conference, Montréal, Canada, October 11-16, 2020

Model

BLSTM

MHSA

RANDOM

CS Score (%)

86.91

87.68

72.80

Table 2: Consistency measure of chord jazzification.

Figure 5: The difference of self-similarity matrices. Left:∆SSM of the BLSTM. Right: ∆SSM of the MHSA. The
origin of the matrices is at the lower left corner.
sequence recurrently, while the MHSA accesses the entire
sequence simultaneously. As a result, the MHSA can capture more structural features than the BLSTM does, leading to more consistent generations. Two examples of the∆SSM are demonstrated in Figure 5. Evidently, there arefewer bright regions in the ∆SSM of the MHSA, indicating that the generation by the MHSA is structurally closer
to the ground truth than that by the BLSTM.
4.4 Generating Jazz Harmony
We train the two-stage chord jazzification model using the
proposed dataset, and generate jazzified chord progressions with input triad sequences derived from the JAAH
dataset. 3 In total, 2210 sequences with 23199 chords weregenerated. To examine the effect of jazzification, we quantitatively analyze the difference between each input triad
and its jazzified counterpart. Particularly, we are interested
in changes with respect to chord coloring: 1) what notes
are added to or omitted from a triad? 2) Is a note otherthan the root being chosen as the bass note?
The result is represented in Figure 6. Around 40% of
the input triads are embellished with a minor seventh (m7).
And the addition of a major seventh (M7) also accounts
for around 3.6%, ranked in the top fourth. These frequentcolorings with a major or minor seventh reflect the characteristics of jazz music in which most triads that appear inlead sheets or fake books can have sevenths added to them.
Moreover, extended chords and inverted chords can also
be found. For instance, the coloring (b2,m7) for a major
triad will lead to a dominant seventh flat ninth chord; andthe coloring None/3 indicates the first inversion of triads.
It is worth mentioning that some generated slash chords are
not inverted chords. An example is shown in the bottomof Figure 6. With the coloring (2,M7)/2, the last triad
Db:M becomes Db:M7/Eb, which can be interpreted as
an Eb dominant thirteenth chord—the dominant chord of
the relative major mode (assuming F is the tonic). In otherwords, this coloring not only breaks the repetitive structure
3 https://github.com/MTG/JAAH

Figure 6: Top: the coloring distribution of the generated
chords (part of the distribution is omitted). Bottom: a generated example. A number after the slash symbol indicatesthe degree of the bass note relative to the root note.
of the input triad sequence, but also implies a new tonality.
In addition to coloring, it can be observed that the linear
progressions of voices are quite smooth, showing that themodel also learns the knowledge of voice leading.
5. CONCLUSION
To learn the interpretation of chord symbols from musicaldata, we proposed chord jazzification, a process of generating realistic jazz-style chord progressions through two musical techniques: chord coloring and chord voicing. Chord
coloring decides a bass and a set of pitch classes for elaborating a triad, while chord voicing arranges the bass andthe set of pitch classes on the piano. We correspondingly
built a dataset which includes coloring and voicing annotations, and hence can be used as the training data of thechord jazzification task. By formulating the chord coloring and chord voicing tasks as classification problems, we
experimentally showed that the two-stage framework is capable of generating plausible chord configurations from asequence of chord symbols.
Chord jazzification has the potential to be applied totwo different yet related practices in music: performing
and composing. For music performing, it is practical anddesirable to interpret the chord symbols on lead sheets
through jazzification. For music composing, the generated
sequence by the chord jazzification model can be regarded
as an intermediate product with which a musician can further create a human-machine collaborative musical work.
In future research, we are planning to include more rhythmical and structural information, such as metrical position,
to better represent the harmonic features, and apply moreadvanced deep learning techniques to improve the chordjazzification task.
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6. ACKNOWLEDGMENTS

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