UNSUPERVISED MELODY STYLE CONVERSION
Eita Nakamura1 , Kentaro Shibata1 , Ryo Nishikimi1 , Kazuyoshi Yoshii1,2
Kyoto University, Kyoto, Japan, 2 RIKEN AIP, Tokyo, Japan

Index Terms— Symbolic music processing; music arrangement; style conversion; statistical music language models; unsupervised grammar induction.
1. INTRODUCTION
Computational understanding of music creation has been a challenge
in artificial intelligence [1–4] and recently it has gathered much attention for use in applications such as automatic music generation[5–17]. Music arrangement for converting the music style, e.g. fromclassical music style to pop music style, is an important creative process to increase the variety of music [18–22]. A few speculations
about this process lead to interesting questions such as ‘How can wecomputationally define and model music styles?’ and ‘What musical characteristics are kept invariant for style conversion to maintainsimilarity between the original and arranged music?’. Here, we address these questions from the viewpoint of statistical learning anddevelop a method for melody style conversion that uses minimal
prior musical knowledge and annotated data.
Style conversion (and other types of music arrangement [9, 14])can be considered in analogy with machine translation: one needs
to generate a melody [sentence] that fits into the target music style[language grammar] and retains the characteristics [meaning] of theoriginal melody [sentence]. To achieve this, we follow the formalism of statistical machine translation [23] and propose a frameworkfor style conversion based on integration of a style-specific language
model representing the target style and an edit model representing
the similarity between original and arranged melodies.
In most work on music generation [8, 11, 12, 17, 20, 22], stylespecific models have been built with training data of a specific ‘music category’ (genre, composer, etc.). However, music categories do
The work was supported by JST ACCEL No. JPMJAC1602, JSPS KAKENHI Nos. 16H01744, 16H02917, 16K00501, and 16J05486, Kayamori
Foundation, and the Kyoto University Foundation.

Clustering

Language model
for style A-1
Language model
for style A-2...

Music category
A

Clustering
Music category
B

Source style

PE (X|X)
Edit modelC C## D Eb E F F# G Ab A Bb B

C C#
# D Eb E F F# G Ab A Bb B

Language model
for style B-1
Language model
for style B-2

PL (X)

Target style

Original melody
in a source style

X
Style conversion

ABSTRACT
We study a method for converting the music style of a given melody
to a target style (e.g. from classical music style to pop music style)based on unsupervised statistical learning. Following the analogy
with machine translation, we propose a statistical formulation ofstyle conversion based on integration of a music language modelof the target style and an edit model representing the similarity between the original and arranged melodies. In supervised-learning
approaches for constructing style-specific language models, it has
been crucial to use data that properly specify a music style. To reduce reliance on manual data selection and annotation, we propose
a novel statistical model that can spontaneously discover styles inpitch and rhythm organization. We also point out the importanceof an edit model that incorporates syntactic functions of notes suchas tonic and build a model that can infer such functions unsupervisedly. We confirm that the proposed method improves the quality ofarrangement by examining the results and by subjective evaluation.

...

1

X
Arranged melody
in a target style

Fig. 1. Framework for style conversion. Style-specific language
models and edit models are obtained by unsupervised learning.
not often correspond to well-defined music styles [24–27]. For example, in the music of ‘Mozart’ or ‘The Beatles’, different styles ofpitch organization (e.g. major and minor modes) and of rhythms (e.g.
quarter-note rhythm, 8th-note rhythm, and dotted rhythm) coexist.
Training data of mixed styles may obscure and even harm the learned
style, leading to unsuccessful music generation [15–17]. Successful
results have been obtained with well-selected and annotated training
data (e.g. with key information) [11, 12, 28], but it requires muchcost to do this for all possible target styles. To reduce the cost of
manual selection and annotation of data, we need a method that canspontaneously discover styles from data [25, 29].
For machine translation, a corpus of bilingual parallel text is typically used for building an edit model. For music style conversion,
the cost of preparing such parallel data is very high, which calls foran unsupervised method for constructing edit models. Studies haverevealed that tonal functions (e.g. tonic and leading tone) are important factors for music similarity, in addition to the geometric distanceof notes [30,31]. In the general situation that source and target styles
have unknown and different musical systems, the challenge is to infer such syntactic functions of notes and the relations between thefunctions in the two styles without using annotated data.
In this paper, we propose a framework for unsupervised music style conversion, focusing on the domain of melodies (Fig. 1).
First, we present a statistical formulation of style conversion basedon integration of language and edit models. Second, we develop amethod for discovering styles from data by clustering characteristics
in pitch organization and rhythms, based on unsupervised learningof a mixture of probabilistic sequential models. For this, we construct a novel Markov model with an architecture to embody metrical structure, transposition-symmetric structure of pitches such asmusical scales, and interdependence of pitches and rhythms. Third,
we build an edit model that incorporates both the geometric distanceof notes and their syntactic functions. This is realized by means ofa hidden Markov model (HMM) that spontaneously learns common
syntax underlying two style-specific music language models. We examine the effect of the proposed method by a subjective evaluation
experiment and by inspecting arranged melodies.

The main results of this study are• A rigid formulation of music style conversion based on the combination of a style-specific language model and an edit model. Previously, an edit model has not been formulated [18–21].
• Music language models capturing meaningful styles such as musical scales and typical rhythms can be obtained unsupervisedly.
• Unsupervised construction of edit models incorporating syntactic
relations of notes without parallel data or annotated data.
• Both the refinements of the music language model and the edit
model improve the quality of melody style conversion.

Classical music
Style 1 (major diatonic)

Style 2 (minor diatonic)

2. PROPOSED METHOD
2.1. Statistical Formulation of Style Conversion
Let us first formalize the problem of melody style conversion. A
m M
melody is described as a sequence ((pmn , smn )Nn=1 )m=1 , wherepmn is the pitch of the n th note in the m th bar and smn is theonset score time of that note, both of which take integer values (Mis the number of bars and Nm is the number of notes in the m thbar). We here focus on pieces in 4/4 time for simplicity and represent score times smn in units of 1/3 s of a 16th note. We assume thatthe number of notes in each bar and the octave ranges of notes are
kept invariant under style conversion. With this assumption, we canrepresent pitches pmn by their relative values (called pitch classes)
qmn ∈ {0, . . . , 11} in each octave range (qmn ≡ pmn mod 12)
and score times smn by their relative values (called beat positions)bmn ∈ {0, · · · , 47} in each bar (bmn ≡ smn mod 48). Thus,m M
we can represent a melody X as X = ((qmn , bmn )Nn=1 )m=1 . A
method for melody style conversion is defined as an algorithm thatmaps an original melody X belonging to a source music style to ane = ((q̃mn , b̃mn )Nm )M
arranged melody X
n=1 m=1 belonging to a target
music style. As necessary conditions for successful style conversion,
we require that an arranged melody matches a target style and thathumans can feel the original melody in the arranged melody.
e
In the statistical formulation, we model the probability P (X|X)
eof the arranged melody X given the original melody X. Similarly as
for statistical machine translation [23], we decompose this probabilee E (X|X),
e where PL represents a target
ity as P (X|X)∝ PL (X)P
language model and PE represents an edit model. The purpose of
the target language model is to describe the characteristics of the target music style and that of the edit model is to embody the (content)esimilarity between melodies X and X.
2.2. Music Language Model
A minimal model for the sequence of pitch classes (pcs) is thepitch-class Markov model (PcMM) defined with initial probabilities0
P (q11 = q) and transition probabilities as P (qmn = q | qmn= q 0 ).
00Hereafter, we write qmn (and similarly smn etc.) for the note thatcomes just before the qmn . Similarly, a minimal model for beatpositions is the metrical Markov model (MetMM) [32, 33] definedwith probabilities P (b11 = b) and P (bmn = b | b0mn = b0 ).
To incorporate interdependence of pitches and rhythms, we
combine the PcMM and MetMM by considering the product spaceof pc and beat position. The transition probabilities are written as00
P (qmn , bmn | qmn, b0mn ) = Ψ(qmn, b0mn ; qmn , bmn ). Hereafter,
to save space, initial probabilities are not written down explicitly
but readers should understand that they are similarly defined. We
call this model a torus Markov model (TMM) since the state spacecan be identified as a grid on a torus S 1 × S 1 with one circle corresponding to the pc space and the other one corresponding to the

Enka music
Style 1 (major pentatonic)

Style 2 (minor pentatonic)

Fig. 2. Learned parameters of the TSTMMixMs. Marginalized
pitch-class transition probabilities are shown as bands.
beat-position space. Note that even with the first-order restriction,
the TMM can induce a range of dependence up to a bar length since
pcs on different beat positions are treated as independent states.
To describe both transpositions (global pitch shifts of entire musical pieces) and modulations (local pitch shifts of phrases or sections of a musical piece), we introduce (local) key variables km ∈{0, . . . , 11} defined for each bar m. For example, when km =0 indicates C major key, D major key is indicated by km = 2.
We suppose that key variables are generated by a Markov model
P (km |km−1 ) and extend the TMM as
P (km = k | km−1 = k0 ) = πk0 k ,

(1)

0
0
P (qmn , bmn | qmn, b0mn , km ) = Ψ(km ) (qmn
, b0mn ; qmn , bmn ).

(2)

To relate parameters for different keys, we impose transposition
symmetry for the model parameters (similarly as in [34]):
πk0 k = π(k0 +`)(k+`) ,
(k)

Ψ

0

0

(q , b ; q, b) = Ψ

(k+`)

0

(3)
0

(q + `, b ; q + `, b),(4)

for any ` ∈ {0, . . . , 11} (additions for pcs and keys are defined inmodulo 12). We call this model a transposition-symmetric TMM(TSTMM). A transposition-symmetric PcMM is defined similarly.
As discussed in Sec. 1, there are commonly several modes ofpitch and rhythm organizations in a music category. With the expectation that these modes can be represented by different parametervalues of TSTMMs, we construct a mixture model by introducing
mode variables ρm ∈ {1, . . . , NM } defined for each bar. For eachmode we consider 12 transpositions indexed by km . The generative
process is now described as
P (ρm = ρ, km = k | ρm−1 = ρ0 , km−1 = k0 ) = πρ0 k0 ,ρk ,(5)0
P (qmn , bmn | qmn, b0mn , ρm = ρ, km = k)0
= Ψ(ρ,k) (qmn
, b0mn ; qmn , bmn ).

(6)

We again impose transposition symmetry as in Eqs. (3) and (4).
This model is called a transposition-symmetric torus Markov mixe
ture model (TSTMMixM). A component TSTMM defines PL (X).
TSTMMixMs can be learned unsupervisedly by the EM algorithm [35]. Although one can use random initialization in princi-

2

...

Output probabilities

C C# D Eb E F F# G Ab A Bb B

1

7

C C# D Eb E F F# G Ab A Bb B

Target style

Edit modelTransition
probabilities

7

Fig. 3. Syntactic functions of notes described in the edit model.
ple, we empirically found that this often leads to unwanted local optima. To solve this problem, we can learn a mixture of transpositionsymmetric PcMMs and that of MetMMs separately and then use
them to initialize the learning of TSTMMixMs.
The two TSTMMixMs learned from the classical music dataset
and the Enka dataset (see Sec. 3.1 for details) are illustrated in Fig. 2.
Here, the PcMMs obtained by marginalizing the component TMMs
are visualized, and the pcs are transposed to make interpretation easier. For the classical music data the two models represent the major
and minor (diatonic) scales, and for the Enka data the two models
represent the major and minor pentatonic scales. Major and minor
scales are similarly learned from the J-pop data. While these scales
are well-known and expected to be extracted from data, it is worthemphasizing that they are here inferred unsupervisedly without any
annotation on the tonic and mode. Similar visualizations for otherstyles are accessible from the accompanying web page [36].
We can also see that different rhythmic styles are learned fromdifferent music categories (see visualizations in [36]). Most notably,
onsets on stronger beats are more frequent in the styles of the classical music, whereas this is not true in the styles of J-pop musicreflecting the presence of frequent syncopations.
2.3. Edit ModelIf note x=(q, b) of the original melody corresponds to note x̃=(q̃, b̃)of the arranged melody, a simple edit model can be defined by(q − q̃)2(b − b̃)2
P (x|x̃) ∝ exp −
exp
−
,
(7)
2σp2
2σr2
where the squared distances are defined in the spaces of pc and beatposition, and σp and σr are scale parameters.
The simple edit model has an essential problem, especially in theunsupervised setup. For example, if one converts a C-major melody
into the minor mode, normally one chooses C minor key for the arranged melody and retains the tonic note (C), which is often used asa closing note. However, with the simple edit model, A minor key(or another minor key) is often selected as the one that minimizes
the geometric distances of notes and then the structure of syntactic
functions of notes (such as tonic) is not retained.
To solve this problem, we construct a refined edit model thattakes into account syntactic functions of notes. To infer syntactic
functions of notes from data unsupervisedly, we apply the techniquedeveloped in [37]. This method uses HMMs with latent states representing functions of symbols, which can be trained using sequential
contexts, i.e. what comes before and after a certain symbol. Writingzmn ∈ {1, . . . , NF } for a latent state (NF is a predefined numberof syntactic functions), the HMM is defined wth transition probabil0
ities P (zmn |zmn
) and output probabilities P (qmn |zmn ), which are
to approximate the probability P (q|q 0 ) of a TSTMMixM.
To construct an edit model connecting two styles, we extend
this model with two output probabilities, one for the source style
P (qmn |zmn ) and the other for the target style P (q̃mn |zmn ) (Fig. 3).

B
Bb
A
Ab
G
F#
F
E
Eb
D
C#
C
C C# D Eb E F F# G Ab A Bb B

(To) Enka style 2 (minor pentatonic)(From) Enka style 1 (major pentatonic)

2(From) Classical style 1 (major diatonic)

1...

Output probabilities

Pitch class correspondence

Source style

Latent common syntax

B
Bb
A
Ab
G
F#
F
E
Eb
D
C#
C
C C# D Eb E F F# G Ab A Bb B

(To) Classical style 1 (major diatonic)

Fig. 4. Learned edit probabilities P (q|q̃) for two sets of source andtarget music styles. Thick rectangles indicate principal scale notes.
0
The latent states and transition probabilities P (zmn |zmn
) are sharedin the two styles to induce the model to represent common syntactic
structure. These probabilities are to approximate both P (q|q 0 ) andP (q̃|q̃ 0 ). This model can induce the following edit probability:
"
#
X Y
e
PF (X|X) =P (qmn |zmn ) P (z|q̃),
(8)
z

m,n

where z = (zmn ) and q̃ = (q̃mn ). The second factor in the lefthand side can be computed by the forward-backward algorithm.
Combining both the distance-based model and the functionbased model, the refined edit model is defined asYe ∝ PF (X|X)
e α1
PE (X|X)
PD (qmn |q̃mn )α2 PD (bmn |b̃mn )α3 ,m,nwhere PD (q|q̃) and PD (b|b̃) denote the two factors in Eq. (7) and wehave introduced weights α1 , α2 , and α3 for the component models.
Examples of learned parameters of the edit model are shown inFig.
maps represent the probabilities P (q|q̃) ∝P 4, where the heat∗∗P(q|z)P(q̃|z)π(πzz is the stationary distribution of z). In thezleft figure, notes of the major diatonic scale are mapped to those of
the minor pentatonic scale, and one can find the mediant and submediant (E and A) are mapped mainly to flat notes (E[ and A[), which
agrees with the musical intuition. In the right figure, the major pentatonic scale is mapped to the major diatonic scale. Notably, the fourthand fifth notes (G and A) of the pentatonic scale correspond to multiple notes of the diatonic scale. The functions of these notes canchange depending on the context; e.g. A before C in the pentatonic
scale can correspond to the leading tone B in the diatonic scale. As
in these examples, we found that the refined edit models obtained byunsupervised learning often matched the musical intuition.
2.4. Algorithm for Melody Style Conversion
An algorithm for melody style conversion can be derived based on
statistical inference of the combination of the language model inSec. 2.2 and the edit model in Sec. 2.3. We first learn one set ofa TSTMMixM for each dataset of the source and target music categories and extract music styles indexed by the mode variable ρsourceand ρtarget . If an original melody X and the corresponding musicstyle ρsource are given, then the key information can be estimated by
the Viterbi algorithm using the source language model.
The target music style is specified by one of the values of ρtarget .
e and its key information with reOne can infer the target melody X
spect to the target language model jointly by maximizing the probee L (X),e which can be done with theability P (X|X)∝ PE (X|X)P
Viterbi-like algorithm. For efficient computation, after the key information of the original melody is estimated we simply transfer thisinformation to the target melody using the edit model since the accuracy of key estimation is high (100% accuracy for our test data).

Original melody##œ œ. œ œ œ œ. œ œ œ œ œ œ.
& # 44 œ œ œ œ

5œ œœ œ œ œ œœ œ œœ œœ œ œ ˙(M1) TMM + simple edit model±1σ SE(M2) TSTMMix + simple edit model

4.5(M3) TSTMMix + refined edit model(M1) Torus Markov model + simple edit model

&

44 œ
#œ

œ

œ œ

œ œ œœ
Jœœ œ œ
J

œ #œ

œ œ #œ œ nœ bœ

œ #œ

œœ˙

Modulation to the subdominant key?
(M2) Transposition-symmetric torus Markov mixture model + simple edit model

œ œ
bb 4
& b b b 4 nœ. œ œœ œ œœ œ œ œ œ nœ. œ œ œ œ œœ œ nœ. œ œ œ ˙Improper closing(M3) Transposition-symmetric torus Markov mixture model + refined edit model&

44 œ œ

œ

œ œ

œ œ œ

œœœ œ œœ œœ œ œ œœ œ

œ œ #œ œ ˙Fig. 5. Examples of melody style conversion. The original melody isa J-pop song. The target category is the classical music and the 2ndstyle (minor mode) is used as the target for methods M2 and M3.
3. RESULTS AND EVALUATION
3.1. Experimental Setup
For numerical experiments, we use three datasets of different music categories, (Western) classical music, J(apanese)-pop music, andEnka music (a genre of Japanese popular song), which were chosen
for the ease of data preparation and subjective evaluation. The classical music data consist of 7133 bars of soprano melodies composedby Mozart, the J-pop data consist of 3878 bars of vocal melodies
composed by a Japanese band ‘Mr. Children’, and the Enka dataconsist of 37032 bars of vocal melodies by various artists [38, 39].
The language model for each music category is trained as follows. First, NPM mixtures of PcMMs and NRM mixtures of MetMMs are learned by the EM algorithm. Then, all combinations of
products of these models are used as initial values for learning the
TSTMMixMs with NM = NPM NRM mixtures. The numbers of
mixtures for the test data are set to (NPM , NRM ) = (3, 1), (3, 2),
and (3, 3) for the classical music, J-pop, and Enka data, respectively.
Out of the learned TSTMMs, we choose two that have large mixture
weights, i.e. most frequently appearing one, and use them as representative styles of each music category. After a few trials, we set
α1 = 0.4, α2 = α3 = 0.8, σp = 0.7, σr = 3, and NF = 7. For
comparison, we implement and test the following three methods:• (M1) Torus Markov model (TMM) + simple edit model• (M2) TSTMMixM + simple edit model• (M3) TSTMMixM + refined edit model
3.2. Example Results
Examples of melody style conversion are shown in Fig. 5, where a Jpop melody is converted to a style of the classical music category. In
the rhythmic aspect, results for the three methods are all successful:tied notes that are typical for J-pop songs are replaced with modest rhythms typical for the classical music style. On the other hand,
three arrangements have different pitch organizations. In the resultfor M1, accidentals in the third bar imply a modulation to the subdominant key, which is not present in the original melody, leading
to an unstable closing. This can be explained by the fact that the
key structure is not described in the TMM. In the results for M2 andM3, the key structure is consistent. However, the result for M2 has
the key of B-flat minor and the closing notes are not properly converted. This can be explained by the fact that functions of notes suchas tonic are not modelled in the simple edit model. One can find nosuch problems in the pitch organization in the result for M3.
Similar tendencies can be found in other examples (see [36]).
Results for M1 often have unnatural key structure and sometimes do
not raise sense of tonality. Results for M2 and M3 are often similar

4
3.5
3
2.5

M1

M2

M3

Style match

M1

M2

M3

Similarity

M1

M2

M3

Naturalness

M1

M2

M3

AttractivenessFig. 6. Result of the subjective evaluation. Main bars indicate means
and error bars indicate 1σ standard errors.
when the keys of the target melodies are same. Otherwise, resultsfor M2 can have unnatural tonal structure, when functions of notesor key structure are improperly transferred to the arranged melody.
3.3. Subjective Evaluation
We conducted a subjective evaluation test to measure the quality ofstyle conversion. 10 evaluators who listen to music more than one
hour a day participated the experiment. Two well-known melodies(8-bar length) are chosen in each of the three categories (classical,
J-pop, and Enka) and are converted to one style of the two different
music categories; in total we have 12 arranged melodies for eachmethod (results are accessible in [36]). After evaluators listened tothe arranged melodies, being informed the target styles but not themethods, they evaluated the following metrics in 6-level scores:• Style match: Does the arranged melody match the target style?
• Similarity: Do you feel the original melody?
• Naturalness: Is the melody natural?
• Attractiveness: Is the melody attractive?
The results in Fig. 6 show that the mean scores of all the metrics are improved by refinements of the method. Particularly, the‘style match’ score improved by 0.5 (p-value < 10−5 , t-test) withthe refined language model (M1 vs M2), and the ‘similarity’ score
improved by 0.28 (p-value = 3.3 × 10−3 , t-test) with the refinededit model (M2 vs M3). The improvements in the ‘naturalness’ and‘attractiveness’ scores are also statistically significant. These results
clearly demonstrate the efficacy of the proposed method.
4. CONCLUSION
Back to our questions, the results of this study indicate that aspectsof music styles such as musical scales and typical rhythms can be
described as clusters defined by statistical generative models, which
can be learned without much relying on expert musical knowledge.
Unsupervised learning of music styles has been studied for use ingenre classification [25, 29], and we showed that it is also usefulfor generating or arranging music. We also revealed the importanceof syntactic functions of musical notes in describing music similarity for the arrangement task, and the effect is particularly enhanced
when the key information must be estimated automatically. The generality of our framework and the promising results suggest that the
proposed formulation of style conversion can also be useful for otherforms of music such as chord sequences and polyphonic music.
The present framework can easily be applied to other musicstyles and we plan to examine the universality of the approach in awide variety of music styles. We found that different music styles are
obtained by different initial values from the same data and the results
are not always interpretable. How to characterize musically meaningful clustering of styles in terms of information measures (e.g.
likelihood) is therefore essential. Determining the optimal numberof mixtures is also an important issue left for future work.

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