SONG2GUITAR: A DIFFICULTY-AWARE ARRANGEMENT SYSTEM FOR
GENERATING GUITAR SOLO COVERS FROM POLYPHONIC AUDIO OF
POPULAR MUSIC
Shunya Ariga
The University of Tokyo
ariga@iis-lab.org

Satoru Fukayama
AISTs.fukayama@aist.go.jp

Masataka Goto
AIST
m.goto@aist.go.jp

ABSTRACTThis paper describes Song2Guitar which automaticallygenerates difficulty-aware guitar solo cover of popular
music from its acoustic signals. Previous research has
utilized hidden Markov models (HMMs) to generate
playable guitar piece from music scores. Our Song2Guitar
extends the framework by leveraging MIR technologies
so that it can handle beats, chords and melodies extracted
from polyphonic audio. Furthermore, since it is importantto generate a guitar piece to meet the skill of a player,
Song2Guitar generates guitar solo covers in considerationof playing difficulty.
We conducted a data-driven
investigation to find what factor makes a guitar piecedifficult to play, and restricted Song2Guitar to use certain
hand forms adaptively so that the player can play thepiece without experiencing too much difficulty. The
user interface of Song2Guitar is also implemented andis used to conduct user tests. The results indicated that
Song2Guitar succeeded in generating guitar solo covers
from polyphonic audio with various playing difficulties.

Figure 1: Overview of the Song2Guitar system.

audio signals. By leveraging Music Information Retrieval(MIR) technologies, we propose a guitar arrangement
system, Song2Guitar, that generates guitar solo covers
from polyphonic audio signals of popular music, whichcontain sounds of various instruments. We also aim
at creating difficult-aware guitar arrangements — i.e.,
generating guitar tablatures having different levels ofplaying difficulty for guitarists. There are three issues thatshould be considered:(1) Generate from polyphonic audio of popular music(2) Difficulty-aware arrangement

1. INTRODUCTION(3) Interface to perform the arrangement result

A guitar solo cover version of an original song adds newpleasure to the music experience of the song. Various
musical elements such as beats, melodies, and harmonies
in an original song are represented in a uniform but
expressive timbre of a guitar. However, a guitar solo
cover of one’s favorite song is not always available, andcreating guitar arrangements requires advanced skills andknowledge and takes a lot of time. If such a guitar solo
cover of any song can be generated from music audio
signals, music listeners can enjoy their favorite songs ina different way, and guitarists who do not have skills forplaying by ear can also enjoy performing any songs ontheir guitars.
The goal of this research is to develop a system that
can automatically generate a guitar solo cover version fromc Shunya Ariga, Satoru Fukayama, Masataka Goto.
Licensed under a Creative Commons Attribution 4.0 International
License (CC BY 4.0). Attribution: Shunya Ariga, Satoru Fukayama,
Masataka Goto. “Song2Guitar: A Difficulty-Aware Arrangement System
for Generating Guitar Solo Covers from Polyphonic Audio of PopularMusic”, 18th International Society for Music Information Retrieval
Conference, Suzhou, China, 2017.

568

An overview of our solutions to address these issues
is shown in Fig. 1. As for issue (1), even if we use
the state-of-the-art MIR technologies, we cannot obtaincompletely-transcribed musical scores from such complex
audio signals. We therefore directly extract importantmusical elements, such as melody lines represented as
F0 (fundamental frequency) contour, beats, and chords,
from polyphonic audio. We then reflect the extracted
elements in generating guitar solo covers by using a novel
extension of a hidden Markov model. As for issue (2), we
conducted a data-driven survey to find what factors makea guitar tablature difficult to play. Based on the survey,
Song2Guitar controls the movement of an index finger andthe number of fingers to press the strings. Finally, as for
issue (3), we designed and implemented an interface thatenables a guitarist to change the degree of difficulty toperform the result. In this paper, we will also discussa desirable interface for generating various arrangement
results and providing training materials for guitarist. The
design of the interface and the results generated by oursystem are available on the web 1 .
1 https://youtu.be/fN4-ibh7ZDI

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2. RELATED WORK
2.1 Creative MIR
Our research is addressed in a Creative MIR approach.
MIR researchers have recently explored creativity-oriented
music technologies by applying technologies developedin the MIR community.
This emerging field isnamed Creative MIR [11] where music analysis andtransformation technologies are used in various creative
applications.
For example, AutoMashUpper [4] isan interactive system that creates music mashups byautomatically selecting and mixing chosen songs. They
achieved automatic mashup by estimating mashability,
which is calculated by using MIR technologies to estimate
various musical elements such as beats, downbeats, andchromagram. Song2Quartet [18] generates a cover songin the style of string quartet by combining probabilistic
models estimated from a corpus of symbolic classicalmusic with the target audio file of a song.

Figure 2: Average movement of an index finger and difficulty
rating of 50 tablatures. The correlation coefficient was 0.55. Theline indicates the linear regression result and the R-squared value
was 0.30.

2.2 Generating playable guitar Solo
In order to generate a playable guitar covers, Hori etal. [7–9] used a hidden Markov model (HMM) to generate
guitar arrangements from a symbolic musical score whileconsidering natural fingerings. Audio signals, however,were not used as the input. By taking audio signals of
an individual separated guitar part as the input, Yazawa
et al. [25] developed an automatic transcription system
specialized for a guitar performance and generated a guitar
tablature by using multi-pitch analysis and playability
constraints. Yazawa et al. [24] then extended their previous
work to transcribe a guitar tablature while considering
acoustical reproducibility and fingering easiness. Even
though guitarist’s proficiency was considered, creating
guitar arrangements from polyphonic music including
multiple instruments was not tackled so far.
Research of automatic fingering decision can also be
regarded as related work of ours. This is because fingering
decision is a sub-problem to generate playable guitar solo,
and the existence of a fingering for a song is a necessary
condition for the song to be playable. Radicioni discussed
in his thesis how to computationally model the fingering inmusic performance [19]. The fingering is often determinedby searching the fingering sequence as an optimal pathsearch problem [20, 21].
Fingerings are represented in a tablature score ortabs, and they are often utilized to analyze and generate
playable scores.
A method to analyze and searchvaluable information in the tablature database has been
proposed [14]. AutoGuitarTab [15–17] generates guitar
music according to different styles of various guitarists bytraining individual probabilistic model using a tablature
database. Genetic algorithms have been used to search
the fingering sequence efficiently to generate an guitar solo
arrangement [22].
Tablature transcription from music audio are also therelated work. MIR technologies such as multi-pitch
analysis and chord recognition have been used to capture

Figure 3: Average number of finger pressuring strings anddifficulty rating of 50 tablatures. The correlation coefficient
was 0.51. The line indicates the linear regression result and theR-squared value was 0.26.

notes and chords in the audio signal. Given those musicelements, dynamic programming or Viterbi decodingwith HMM has been leveraged to output reasonable
fingerings [1, 10, 12].
3. PLAYING DIFFICULTY OF A GUITAR SOLO
3.1 Analysis of guitar tablatures
We first investigated what are the factors that affect theplaying difficulty. We collected tablatures from a website distributing classical guitar music 2 . These tabs were
written in a plain text format and did not have uniformity
in data structure. We therefore implemented a parser toretrieve structured tab information. Since Song2Guitar
assumes only the standard tuning (E-A-D-G-B-E), we
excluded tabs that were instructed to play in anothertuning. Furthermore, we also excluded scores for guitar
duo as we focus on guitar solo covers.
2 http://www.classtab.org

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Proceedings of the 18th ISMIR Conference, Suzhou, China, October 23-27, 2017
For each tablature, we considered and calculated two
factors: the average movements of the index finger ofa hand to hold the guitar, and the average number offingers to press the strings. The position of index finger
is determined as follows: if the fingering contains a barre,
we use the fret position of the barre, otherwise the positionof the index finger is set to be the minimum fret number
among the frets being pressed. We hypothesized that these
two factors affect the playing difficulty of a guitar solo.
3.2 Subjective test to evaluate the playing difficulty
We verified our hypothesis by asking proficient guitarists
to rate the difficulty of the tabs. Five independent
raters subjectively evaluated the difficulty with a 7-point
Likert scale (1: easiest – 7: most difficult). The raters
were instructed to consider only the complexity of thefingerings of the left hand. As they respectively rated
randomly-selected 10 tabs, we consequently obtained
50 ratings. Fig. 2 and Fig. 3 show the plots of ourhypothesized features (average movement of an index
finger, average number of fingers to press strings) and theresult of difficulty ratings. The correlation coefficients
calculated with these two features and the ratings were0.55 and 0.51, respectively. We also conducted a linear
regression on data. The regression results are also shownin Fig. 2 and Fig. 3. R-squared values for these tworegressions were 0.30 and 0.26, respectively. These results
indicated that the tabs were evaluated to be more difficult
when the values of both features get larger.
4. CREATING DIFFICULTY-AWARE GUITARARRANGEMENTS FROM MUSIC SIGNALS
4.1 Our problem setting
In solving the problem of generating guitar solo covers
from audio signals of popular music, we want to maintainand reproduce major characteristics of an original song in a
generated guitar solo cover. The followings are the major
characteristics that most songs in popular music have incommon.
• It contains a clear melody line that is performed bya vocal part.
• It contains a bass line corresponding to a chord
sequence.

4.2 Guitar Arrangement by using HMM
We start from reviewing how HMM can be applied to
the fingering decision problem. Suppose we have acollection of guitar music scores, and we want to model
this collection statistically. This means that we need toobtain a function that returns high probability if the music
seems to be included in the guitar music collection, andlow probability when the music is obviously not a guitar
music. Designing a generative model is one method toachieve this.
The generative process of a guitar music is apparently
the process of performing a guitar instrument. When theguitar is played, one hand holds the neck and its fingers
press strings on the frets. Fingers of the other hand pluck
the strings, and eventually a sound is generated. We cansee that the output sound is determined when the states ofboth hands are determined.
In terms of the hand to press the strings, it is less likely
to observe a drastic change of the hand form in a very
short duration because of physical constraints of the humanbody. It is also unlikely to observe a long distance move ofposition of a hand to hold the neck of a guitar. Since these
two aspects are relationships between the current and theprevious state of holding the neck, we can model them by
the first-order Markov chain. Let Xt be the fingering attime t. We can define a probability for observing fingering
Xt+1 as P (Xt+1 |Xt ). Xt contains four components eachof which corresponds the state of each finger of the left
hand. Each component has two values: one indicates thestring index of a guitar to put pressure on, and the otherindicates the fret number to put the finger on. Fret number
0 indicates that the finger does not touch any string.
The output sound is audible when strings are plucked.
The sounding notes are biased by the fingering. Let Yt
be the set of notes played at time t, such as set consistingof C3, E4 and G4. Yt follows a probability distribution
P (Yt |Xt ) which models the playing notes biased from thefingering.
A guitar performance can be realized as a time sequenceof both the fingering (X1T = X1 · · · XT ) and the plucking
of strings at each fingering (Y1T = Y1 · · · YT ). Notethat T indicates the length of a sequence, not indicatingtransposition. The probability of generating notes fromthe given fingering is calculated by the product of theseprobabilities as:T Y
P Y1T |X1T =P (Yt |Xt ) P (Xt |Xt−1 ) .

(1)

t=1

• It gives a rhythmic groove emerged from sounds ofrhythmic instruments.
Although previous work of generating guitar arrangementsfrom symbolic musical scores [7] formulates the problem
by using HMM, it have not tested with audio input. To
generate from polyphonic music audios of popular music,
we formulate the problem by a novel extension of HMM.
We also propose how we can generate various results withdifferent levels of playing difficulty for guitarists.

Since the fingering cannot be observed from theguitar music afterwards, X1T is hidden and therefore
this probabilistic model is called as hidden Markov
model. P (Yt |Xt ) is called as emission probability, andP (Xt |Xt−1 ) is called as transition probability. By using
Viterbi decoding, we can efficiently estimate the most
likely fingerings which maximize the likelihood in termsof X1T [23].
Now we can extend the generative model discussed
above to let the model generate music that is not necessaryProceedings of the 18th ISMIR Conference, Suzhou, China, October 23-27, 2017
to be guitar music, but could be arranged into guitar music.
In particular, the emission probability is revised so that themodel can output notes which are octave higher or lower
than the notes of the actually played pitches by plucking
the string. As Hori et al. formulated in their works [7–9],
the emission probability is set to allow the number of notes
more than the guitar can perform simultaneously.
By executing the Viterbi algorithm with this extension,
we can obtain a sequence of fingering from not only
guitar music but also from any music which is not
originally composed for a guitar. Since the existence ofproper fingering is the necessary condition for a guitar
arrangement, we can generate a guitar solo cover by theabove extension of fingering decision formulation.
4.3 Creative MIR Approach to Guitar Arrangement
4.3.1 Leveraging MIR technologies
One of the novelties of this research is the further extensionof the generative model so that it can generate guitar
solo covers from polyphonic music audios by leveraging
MIR technologies. Since the melody, beats, and chords
are main elements that can be reflected in a guitar solo
arrangement, it is not necessary to try to obtain all notes
by using multi-pitch analysis. We therefore use methods
that can estimate the melody (F0 contour), beats, andchords in polyphonic audio including drums. We show thatthese methods developed in the MIR community largely
contribute to generating a guitar solo cover.
The melody estimation here assumes that the melody
is sung by a singer. We first extract a singing voice trackby using an existing singing voice extraction method. Wethen applied a melody estimation method proposed byGoto [5] to obtain the F0 contour. We also smooth the F0
contour by using an FIR low-pass filter with 5 Hz cutoff
frequency in order to remove the vibrato. To discretize
the F0 contour into musical notes, we used beat estimation
results to approximately obtain what musical note is played
as the melody line at every 16th note.
Chord estimation provides chord labels (chord names).
Since the label contains “on-chords” such as “C/E” or “Con E”, we literally use the bass note described in the chordlabel. We used a chord estimation method developed by
Korezeniowski et al. [13], which is available in Madmom
or an audio signal processing library written in python [2].
Finally, the beat estimation plays an important role ingenerating a guitar solo cover. The beat estimation results
give us a set of segments corresponding to quarter notes.
By dividing every quarter note into four parts, we obtaina finer set of segments with the resolution of 16th notes.
These 16th-note segments can be used to quantize theF0 contour of the melody line as explained above, andalso quantize chord estimation results. In other words, allnote lengths extracted from the audio are quantized into
integer multiples of the 16th-note duration. We used a beatestimation method proposed by Böck et al. [3] which isalso available in Madmom [2].

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4.3.2 Emission probability
Based on the results of these estimation methods, we set
the emission probability as follows so that the HMM canhandle music audio to generate a guitar solo cover:P (Yt |Xt ) ∝ Pchord (Ct |Xt ) + Pmelody (Mt |Xt )+ Pbass (Bt |Xt )(2)

The subscript t denotes the index of the onset. Since theonsets are not apparent in audio signals, we regard thetiming of a sudden increase of power in singing voice andthe timing of every chord change as onset timings. The
onset timings are discretized by using the beat estimation
result. Y denotes the audio segment with 16th-note
duration. C, M , and B are the chord label, melody pitch,and bass pitch, respectively. X denotes the fingering topress the fret, which is a set of the pressing position of eachfinger including open strings. Open strings are represented
as pressing the imaginary 0th position of the fret.
Probability Pchord is set based on how the current
fingering achieves the chord observed at the time. For
example, when the fingering is given to play “C, E, G”,
the probability for observing “C maj7” would be high, but
the probability for observing “F# maj” would be low. Thiscan be measured by the number of elements in intersection
between the set of notes derived from the fingering and thechord label. In this example, the set of notes for “C maj7”
is “C, E, G, B”, and the set of notes for “F# maj” is “F#,A#, C#”. The probability for observing “C maj7” is higher
since the intersection has “C, E, G” (3 elements) whereas“F# maj” has no elements as intersection. We implemented
this as follows:
Pchord (Ct |Xt ) ∝ exp (−α · # (N (Ct ) ∩ N (Xt ))) (3)where N (·) denotes the set of consisting notes of chordlabel or guitar fingering. We adjusted the parameter to be
α = 3.0 in our experiments.
Probability Pmelody is designed by considering how
the highest note of the playing notes with the fingering
is relevant to the melody pitch observed in the acoustic
signal. Let M (Xt ) be the highest note could be played
from the current fingering. The probability can be designed
as:(1.0 (Mt = M (Xt ))ε1 (Mt = M (Xt ) + 12n (n 6= 0))
Pmelody (Mt |Xt ) ∝ε2 otherwise(4)
where the parameters are set as ε1 = 0.3 and ε2 = 10−5 .
These parameters were set heuristically by iteratively
generating and subjectively evaluating the results.
Finally, probability Pbass is set similarly to Pmelody . Let
B (Xt ) be the lowest note that could be played from thecurrent fingering Xt . The probability is designed as:(
1.0 (Bt = B (Xt ))ε1 (Bt = B (Xt ) + 12n (n 6= 0))
Pbass (Bt |Xt ) ∝ε3 otherwise(5)
where the parameter ε1 shared the same value as in
Pmelody , and ε3 was set as ε3 = 0.0027 in our experiment.

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Proceedings of the 18th ISMIR Conference, Suzhou, China, October 23-27, 2017

Figure 4: User interface of the Song2Guitar system.

4.3.3 Transition probability
For setting transition probability P (Xt+1 |Xt ), webasically followed the formulation by Hori et al. [7]. We
defined the transition probability given the time interval dt
between onsets as:1|I (Xt+1 ) − I (Xt ) |P (Xt+1 |Xt ) ∝exp −λm
2dt
dt
1
1
×
×
1 + I (Xt+1 ) 1 + W (Xt+1 )1×
1 + N (Xt+1 )(6)where I (X) denotes the position of an index finger whenholding the fret of a guitar with a fingering X. W (X)denotes the length between the leftmost fret used and therightmost fret used under the fingering X. N (X) denotes
the number of fingers used to achieve the fingering X.
4.4 Controlling the Degree of Difficulty
Based on the survey described in section 3, we determined
the following two parameters to control the playing
difficulty for generating a guitar solo cover: the average
movement of the index finger of a hand to hold the guitar,which is denoted as amove , and the average number offingers to press the strings, which is denoted as astring .
Song2Guitar supports three different levels of playing
difficulty: EASY, NORMAL, and HARD. To create theselevels by changing amove and astring , we adaptively
restricted the use of fingering X according to the following
constraints:
EASY : amove ≤ 2.0 && astring ≤ 2.0
NORMAL : amove ≤ 4.0 && astring ≤ 3.0(7)

HARD : use all available fingerings.
5. INTERFACE DESIGN OF SONG2GUITAR
Song2Guitar aims at not only generating a guitar solo
cover automatically but also enabling a guitarist to easily
practice and perform the generated result. Fig. 4 shows themain interface of Song2Guitar. The design of the interface
and the results generated by our system are available on the
web 3 .
Because the tablature form is more intuitive than themusic score, Song2Guitar visualizes the tablature scoreof the generated cover song. This tablature score scrolls
automatically while playing since a guitarist uses both
hands to perform the guitar and no hands are left to control
the system.
We also implemented an interface to control the playing
difficulty of the results. When we aim at creating playable
arrangements for human guitarists, it is important tocontrol how difficult the generated score is. Guitarists
would be discouraged if the score is too difficult or too
easy for them.
The tablature shown in the interface contains additional
notations to make the practice and performance easier.
Numbers in colored circles on the strings indicate the fretthat the guitarist should press on the string. The indicatorwith a purple vertical line (in the left of Fig. 4) shows thetiming to pluck the string.
The interface of Song2Guitar also supports
non-proficient guitarists to find the position to press
the indicated frets. Usually a guitarist needs to prepare
the hand form to press the fret in advance of plucking thestrings. Even though the tablature score is shown, a novice
guitarist often gets stuck in keeping finding where to put
their left hand to hold the neck of a guitar. This is because
the tablature usually indicates only the fingerings on thefrets, but does not indicate the position of the left hand to
hold the neck of the guitar. Therefore we implemented
to show small diagrams (shown below of the tablature in
Fig. 4) representing how to place the fingers in a similar
fashion to a guitar fingerboard. The diagram is shown
when there is a position change in a hand to hold the frets.
The Song2Guitar interface also supports ademonstration mode which playbacks the generated
tablature by using synthesized guitar sounds so that music
listeners can simply enjoy the system output.
3 https://youtu.be/fN4-ibh7ZDI

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6. EVALUATION
To evaluate how the performing difficulty varied among thegenerated guitar solo covers, we conducted an experimentin a qualitative evaluation approach.
6.1 Experimental setting
We asked a guitarist who is proficient in playing theclassical guitar to participate in the evaluation. The
guitarist was male and 24 years old, and had an experiencein playing the acoustic guitar (both steel and nylon strings)
for around six years.
We used RWC-MDB-P-2001 No.7 from the RWCMusic Database [6] to generate a guitar solo cover.
We generated three different covers with different levelsof difficulty (EASY, NORMAL, and HARD) by using
Song2Guitar. To focus on evaluating varying difficulty,
we manually corrected estimated beats and chords before
generating them.
The guitarist was first asked to practice each score for
15 minutes. Since the duration of generated pieces wereabout five minutes long and it was too long to practicethe entire song, we asked the guitarist to practice only
the intro, the first verse, and the chorus section. Afterthe practice, we asked the guitarist to play all designatedsections of each cover. Finally, we conducted a shortinterview to obtain comments on Song2Guitar. The
obtained comments were originally in Japanese, and they
were translated into English as shown in this paper.
6.2 Evaluation results
We obtained a comment indicating that the participant
enjoyed using the system:
I think this is a really great app.. I can play a song
endlessly, and it was like some kind of a game.
We also found a comment to indicate that our systemgenerated covers in three different levels of playing
difficulty (EASY, NORMAL, and HARD):
Well, playing difficulties were appropriate, difference
between NORMAL and HARD makes sense.
Although we intended to make three covers as getting
gradually difficult, the participant commented that theplaying difficulty of EASY and NORMAL were reversed:
EASY score was not easy, it was more difficult than
NORMAL one, for me. The HARD score was like inthe middle of NORMAL and EASY.
The participant reported why “EASY score was not easy”
as follows:
I guess that it’s easier when it consists of chords(multiple notes) moderately than full of simple notes.
Chords are the basic form, and I can figure out how to
do fingering in my mind. When only two notes appear
in the tab, of course, I can figure out the fingerings,
however, it didn’t go well [...]
This comment indicated that smaller number of notes are
not always easy to play. The fingering of chords provides abasic form, and a guitarist is more familiar with it than theother irregular fingerings for fewer notes.
The participant also pointed out the playing difficulty
comes from the note value of the generated results.

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The difficulty is, I think it’s easy if all notes wereeighth note. Sixteenth note is difficult to figure out thetiming.
He also indicated the issue in the interface design:
It’s hard to understand beats and timings of notes with
the interface. I appreciate if every half beat werehighlighted, somehow.
7. DISCUSSION
We confirmed that Song2Guitar was able to generate
guitar solo covers from polyphonic audio of popular musicby leveraging MIR technologies. We found that theHMM formulation to generate guitar solo combined withestimation of melody (F0), beats, and chords was effective
even from music audio which multi-pitch analysis cannot
be sufficiently applied to.
We also found that Song2Guitar was able to generate
output with different playing difficulties. We introduced
two parameters: the average movement of an index finger
and the average number of fingers to press the strings,
to control the playing difficulty. The evaluation results,
however, suggested that there would be more factors that
affect the playing difficulty. One possible factor for
determining the difficulty is the familiarity of particularfingerings such as chords.
The interface of Song2Guitar enabled the player topractice and perform the generated result. The comments
obtained in the experiment revealed that the rhythms of thegenerated covers were sometimes hard to recognize. Theinterface did not visualize the timing except for showing
the indicator bar. Highlighting half beats would help theplayer recognize the rhythm much easier.
The future work of this research is to enable
Song2Guitar to generate cover songs in real time
considering the player’s proficiency. Conducting an
objective evaluation is also included in future work.
Since the generative model is designed as a probabilistic
model, we can verify the fingering model by calculating
cross-entropy.
8. CONCLUSION
We proposed Song2Guitar that generates guitar solo
covers from polyphonic music signals of popular songs.
The formulation using HMM was combined with MIR
technologies so that it can generate covers considering
the melody, bass and rhythm of the songs. Furthermore,
Song2Guitar generated covers with different levels ofplaying difficulty. The interface was implemented and a
guitarist succeeded in playing different guitar solo covers.
In the future, cover song generation from music audio
signals will be further improved by leveraging other MIR
technologies.
9. ACKNOWLEDGEMENT
This work was supported in part by JST ACCEL Grant
Number JPMJAC1602, Japan.

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