ICSP2014 Proceedings

AutoLeadGuitar: Automatic Generation of Guitar
Solo Phrases in the Tablature Space
Matt McVicar, Satoru Fukayama, Masataka Goto

Abstract-We present AutoLeadGuitar, a system for automati­

Training tablature, chords, keys and phrases

cally generating guitar solo tablatures from an input chord andE minorkey sequence. Our system generates solos in distinct musicalphrases, and is trained using existing digital tablatures sourced
from the web. When generating solos AutoLeadGuitar assigns
phrase boundaries, rhythms and fretboard positions within aprobabilistic framework, guided towards chord tones by two user­
specified parameters (chord tone preference during and at theend of phrases). Furthermore, guitar-specific ornaments such as
hammer-ons, pull-offs, slides and string bends are built directly
into our model. Listening tests with our model output confirm
that the inclusion of chord tone preferences, phrasing, and guitar
ornaments corresponds to an increase in user satisfaction.

Keywords--Computer generated music, Music information re­trieval

HIS

I.

. ..

INTRODUCTION

T

paper introduces a method of algorithmic composition
for the guitar. Guitar parts in popular music can broadly be
split into rhythm (outlining the main harmony/rhythmic pulse)
and lead parts (harmonies/solo breaks). Naturally extending ourprevious work [1], the current paper is focused on the latter.

•

INPUT: Digital Symbolic guitar
solo (MusicXML)

INPUTS: Key, downbeat­synchronized chord sequencer �� i[':r:.JI�� t.'· t.: [,i:.r
�i. ' "'# '�"'-r-" L'1

I;

Our main motivation for this work is that automaticallygenerated guitar solos have the potential be used in situations
when a popular music composer would like a guitar solo in apiece but lacks the necessary familiarity with the instrument
to compose one. Furthermore, we believe that automaticallygenerated content could be used as a pedagogical aid, to help
amateur musicians learn different approaches to playing over agiven chord sequence. This work could therefore be considered
an example of 'Creative MIR' [2], whose goal is to use real­world applications to transfer Music Information Retrieval
results beyond the immediate research community.

jr "

A7
D7

B. Challenges and proposed solutions
There are several challenges which must be overcome in theautomatic generation of guitar solos. First, algorithmic melody
composition (of which we consider the current work to be asubtask) has been said to be challenging to evaluate without
the presence of a musical context [3]. Inspired by previous
work [4], [5], our system counters this by using a key andchord sequence as a user-defined input, and guides the melody
towards chord tones to enhance musicality and realism.
All authors are at the National Institute of Advanced Industrial Science andTechnology (AIST), Tsukuba, Japan and can be contacted at: mattjamesm­
cvicar@gmail.com, s.fukayama@aist.go.jp, m.goto@aist.go.jp.

599

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II! �" �J ("I �! 'OUTPUT: Phrase boundaries
l'J r.J � r r" ; gr r[V-' U I

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A. Motivation

978-1-4799-2186-7114/$31.00 ©2014 IEEE

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••

--. .

OUTPUT: Guitar solo tablature(MusicXML).. r

lj"C''rJ 'rdll;.!

oJ" " ",,

.

' ''
•

•

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Lid-=:,
II,, " "•

Fig, l. Flowchart of AutoLeadGuitar's main processes_ Training data (digitaltablatures annotated with chords, keys and phrases) are used to create models
for the detection of phrases in existing solos, and for the generation of novel
solos when complemented with an input chord and key sequence,

We believe one of the key concepts in generating realistic
guitar solos to be etlective phrasing. Indeed, it has recently
been shown that composing musical events in phrases facilitates
perception and analysis by the human auditory system [6], andis essential in conveying expressiveness in music [7]. Phrasing
is particularly challenging for guitarists when compared toother solo instruments (such as the wind instrument family) as
guitarists cannot rely on pauses for breath to construct lyricalphrases. To better understand the phrasing techniques used bylead guitarists, the first stage of the current work is therefore an
analysis of existing guitar solos, leading to a statistical modelfor the detection of phrases in digital scores. An algorithm forthe generation of guitar solo phrases is subsequently introduced,
defined by parameters learned from an existing corpus.

2

Generating music for the guitar algorithmically is also
particularly challenging owing to the physical layout of the
instrument. Specifically, the pitch ranges for guitar strings
significantly overlap, meaning there is more than one position(string and fret number) to play most notes. Tablature notation(or simply tab, plural tabs), which explicitly notates the positionin which to play each note in a piece, was developed toovercome this ambiguity. Existing compositional models which
output traditional notation rather than tab would therefore
necessitate an automatic fingering or arrangement (see II-B). To
combat this and to exploit attractive aspects of the instrument
such as string bends, hammer-ons, pull-offs, and slides, ourmodel composes directly in the tablature space. A high-level
outline of our proposed techniques is shown in Figure 1.
C.

Paper structure

The remainder of this paper is organised as follows. In Sec­tion II we survey the literature on computer-aided composition,
algorithmic guitar fingering/arrangement and phrase boundary
detection. Section III outlines our phrase boundary detection
and compositional model, which are evaluated in Section IV.
Finally, we conclude the work and discuss areas of future
research in Section V.

level. A data -driven approach to the same problem wassuggested by Radisavljevic and Driessen [16], whose path
difference learning learns the weight costs of a particular
playing style based on labelled tabs. Genetic algorithms havealso been explored as a means of efficiently exploring the large
search space created in the fingering decision problem [17].
Hori et al. [18] designed an input-output Hidden Markov Model(HMM) for the automatic arrangement of guitar pieces.

C. Automatic phrase boundary detection
Existing phrase boundary detection methods are mostly basedin the audio domain. Aucouturier and Sandler [19] detectedchanges in spectral similarity to identify phrase boundaries,
whilst Cheng and Chew [20] instead used loudness andexpressive parameters. An unsupervised HMM was presented
by Kim and Weinzierl [21] which used cues including noteduration relative to the preceding note. Pearce et al. conducted
a comparison and evaluation of symbolic data-driven and rule­
based phrase detection algorithms [22], finding that a hybrid
model was able to attain an f-measure of 0.66.
III.

METHODS

A. Identification of guitar solo phrase boundaries
II.

RELEVANT LITERATURE

A. Computer-aided melody generation
Algorithmic composition has a rich and varied researchhistory (see, for example, [8], [9] and the survey [10]), of which
computer-aided composition [5], in which the compositional
task is split between human and computer, is an interesting
subtask. The interaction between human expert and the machinein computer-aided composition may occur in a fixed order, or
involve several iterations back-and-forth [11].
The generation of melodies given a chord sequence is aparticularly popular topic in computer-aided composition, aparticular example of which is John Biles' GenJam [4], agenetic algorithm for the generation of jazz solos. GenJam
takes a chord sequence as input and outputs an improvisedsolo, with melody phenotype fitness optimized over several
iterations via user feedback. Pachet and various collaborators[12], [13] have tackled generation of sequences (including
melodies) via Markov processes with hard constraints such as"end the phrase on this note" and with great care taken to avoid
plagarism in model output.

B. Automatic guitar fingering and arrangement
As mentioned in Section I, the layout of the guitar meansthat mapping a given musical score to string and fret positions
is non-trivial, and may not even be possible. Algorithmicallymapping a score to a tab and minimally altering a score toensure it is playable on the guitar are referred to as algorithmic
fingering and arrangement respectively.
Sayegh first considered the problem of algorithmic fingering
for stringed instruments [14], introducing an optimum pathparadigm algorithm, later developed by Radicioni [15] tominimise fingering difficulty at the phrase, rather than global

600

We formulate phrase boundary detection probabilistically via
a supervised Hidden Markov Model, with observed states as
symbolic note properties (pitch, note duration etc.) and statescorresponding to the underlying phrase structure.
When inspecting existing solos, we noticed that somephrases contained brief rests and that in others phrases ran
consecutively without rest, meaning that the presence of arest is neither necessary nor sufficient to segment phrases.
Furthermore, we noticed that phrase ends had distinct charac­
teristics which made them amenable to automatic identification,
most notably that they tended to end on chord tones and on
strong metrical position, and had duration significantly longer
than their predecessors. On the basis of these observations,
our model has three hidden states at each time point t:
Xt E {no phrase, phrase, phrase end} (note that phrase starts
can be uniquely defined as following immediately from a 'nophrase' or 'phrase end').
Hidden chain model parameters for the HMM (initial
distribution of phrases Ani (Xl) and phrase-to-phrase transitions
Arans(XtIXt-l» were set using a fully-labelled dataset using
maximum likelihood estimation (MLE). We set the observation
probabilities Pobs(YtIXt) based on the following assumptions.
First, that phrases are unlikely to contain rests. Second, phrases
end on strong metrical positions and harmonically stable pitches.
Finally, phrase ends are likely to have a duration significantly
longer than their predecessors.
To model these properties mathematically, we extracted thefollowing properties of each symbolic note: whether the note
was an onset or a rest, the metrical position (quantized tosixteenth notes), the pitch relative to the underlying chord, andnote duration relative to its predecessors (calculated by taking
the ratio of the note duration and the median duration of theprevious three notes). We then set the probability of witnessing

3

note Y given state x at time t as the product of y's rest, metricposition, relative duration, and chord tone probabilities:

Pobs(Ytlxt)=

Pr(Ytlxt)Pm(Ytlxt)Pd(Ytlxt)Pc(Ytlxt).

Pr, Pm and Pc were estimated from training data directly fromnormalised histogram counts. Pd represents a waiting time andis therefore naturally modelled as a Gamma distribution, theshape and scale parameters of which were estimated using
MLE. The joint probability of a hidden state sequence x(Xl, . . . , XT) and observed note sequence y(YI, . . . , YT)
given these parameters can then be computed via:==T

P(x, y)=Ani (xI)

II Ptrans(xtlxt-l)' Pobs(Ytlxt)

t=2

The state sequence which maximises this quantity:y*=argmaxP(x,y)
ycan then found efficiently via the Viterbi algorithm [23].

B. Generation of phrase boundaries
Our phrase generation algorithm is intuitive and we believe
mimics the creative process guitarists use when improvisinga solo. It is outlined in Figure 2. Given a set of measuresover which to play, our algorithm begins by choosing a metricposition in the first measure (phrase onset selection, A) and aphrase duration (phrase duration selection, B). A phrase onset
is then chosen for the next phrase (A), chosen from whateverremains of the current measure and the next full measure. This
process is then repeated until the solo measures are exhausted.
We implemented this algorithm by calculating a phrase start
probability Pps and phrase duration distribution Ppd from ourtraining data. We found that Ppd was well approximated by anormal distribution, with mean usually a little over one measure.
Phrase onset probabilities carried over multiple measures (suchas the second iteration of algorithm stage A in Figure 2) werenormalised to meet the probability criterion.
C.

Generation of rhythms

Phrase rhythms were set from a note onset bigram model,
which specified the probability of an onset at each sixteenth
note given the last onset position. These probabilities were
calculated from our training data and collected into a matrix:

R E IR16X16, such that

16

L Ri,j

j=l

=

1.0

for i

=

1,... ,16.

The above-diagonal elements of R represent transitions further
into the current measure, elements below and on the diagonal
represent transitions into the following measure. The first onsetof each phrase was set to be start of the phrase itself, with arandom walk over the rows of R used to generate subsequent
onsets within the phrase, until the phrase was exhausted.
In practice we found that favourable results were obtainedby extending the offset of the final note of a phrase to theonset of the following phrase, increasing the duration of the
final note in the phrase and mimicking the behaviour seen in
existing solos (recall Subsection III-A).

601

Fig. 2.
AutoLeadGuitar's phrase generation algorithm, which alternates
between phrase onset selection (stage A) and phrase duration selection (stageB) until the designated measures are exhausted.

D. Phrase pitch state space:
To set the pitch for each note onset, we constructed a bigram
model in the tablature space. To incorporate string bending, afacet we believe to be important in expressive guitar playing,each unique state in the pitch model consisted of a (string,
fret, bend) triple. Bends were measured in integer number ofsemi tones (0 for unbent states). This data was collected in amatrix P E IRlsl x lSI, where lSI denotes the number of unique(string, fret, bend) state triples seen in the data.

E. Key normalization
To maximally exploit our training data and increase oursystem's generalisation potential, we transposed our data into
two canonical keys (C major/A minor) before normalising therows of P. The reason we believe a key dependent, rather thanchord dependent model to be valid is that lead guitarists in theblues and rock genre frequently improvise using the ubiquitous
pentatonic scale (pitch classes A, C, D, E, G in the key of C
major/A minor), as observed by Flor and Holder [24]:"For rock and roll and blues songs, knowledge of pentatonic
scales and a song's key is usually sufficient for improvising. "P was 'untransposed' into a user-specified key during testing.
F

Highlighting chord tones

We found that by simply taking random walks over P, atthe end of phrases our model frequently ended on dissonant or
unresolved tones, resulting in a slightly frustrating listening
experience. Similarly, we noticed that interesting non-diatonic
chord tones were unfortunately rarely highlighted. One method

4

to counter this suggested to students of the guitar is tohighlight specific chord tones in solos:" . . . try starting your idea on a chord and aim for another,meeting the change with a strong, chord-defining pitch. " [25]TABLE II.
PHRASE BOUNDARY DETECTION PERFORMANCE. COLUMNS
2-4: INTER-ARTIST TESTING. COLUMNS 5-7: INTRA-ARTIST TESTING.

Guitarist

P(St+lISt)=

wP(St+lISt) + (1 - w):ll.(St+1 E Ct+l),

where Ct+l are the pitch classes of the chord at time t + 1 andw E [0,1] is an interpolation weight. Recalling from Subsection
III-A that many phrases ended on chord tones, we set w/3
when the note was the final note in a phrase and, otherwise,
where /3" E [0,1] and generally /3 >,.

r

Performance

7

p

Intra-artist
r

7

Slash

0.74
0.79
0.78
0.73

0.61
0.55
0.58
0.47

0.67
0.65
0.66
0.57

0.81
0.79
0.82
0.70

0.55
0.50
0.64
0.52

0.66
0.61
0.72
0.59

Total

0.73

0.54

0.63

0.70

0.54

0.63

Eric Clapton

We incorporated this behaviour in our compositions by added
two parameters to guide the random walk process towards chordtones. Specifically, given the current pitch state St we calculate
the probability of the next state St+l by interpolating between
the t-th row of P, and a function which is 1.0 if St+l is achord tone and 0.0 otherwise:pInter-artist

Jimi Hendrix
Jimmy Page

Downbeat-synchronised chord sequences, key sequences andphrase boundaries were then annotated for each solo by theauthors. In total we found our dataset to contain 1,266 guitar
solo phrases consisting of 11,854 individual notes.

=

G. Exploiting guitar-specific ornaments
When two subsequent states occur on the same string, a
guitarist has the option of transitioning between them in atleast three novel ways. Briefly defining some terminology: aslide between states is a simple glissando from one fret to
the next, a hammer-on is sounded by 'hammering' from one
fret on a string to a higher fret on the same string without
plucking/picking, and a pull-off is formed by 'pulling' one
finger from the fretboard onto a lower fret.
The probability of any one of these ornaments occurring
between any two states in the model was learnt from data using
the same methodology above. We stored the probabilities in(fairly sparse) key normalized hammer-on/pull-off and slide
transition matrices HP, S E IRlsl x lSI for each ornament. Notethat hammer-ons and pull-offs may be stored in a single
matrix since they are only possible on state transitions strictlyincreasing/decreasing fret numbers respectively. A hammer­
on/pull-off between states with indices i and j was thenincluded with probability HPi,j. If no hammer-on or pull­
off was included, a slide was between the states was addedwith probability Si,j.
IV.

EXPERIMENTS

A. Data collection
We collected digital guitar tablatures to train our model fromthe user-generated content website GuitarPro.netl , choosing ten
songs each from four popular guitarists in the blues/rock style:
Jimi Hendrix, Eric Clapton, Jimmy Page, and 'Slash' (Saul
Hudson). Guitarists were chosen according to tab popularity(number of tabs), with songs chosen which were mostly in
C Olmnon time and in standard tuning (or tuned sharp or flat 1
semi tone, which is easily transposed). When more than one
tab was available for a song, the most accurate or completetab was chosen. Each tab was then converted to MusicXML
format via GuitarPro for automated analysis.
t http://www.gprotab.neti

602

B. HMM for guitar solo phrase boundary detection
We tested our HMM for phrase boundary detection using
cross-fold validation. Unsure if the guitarists in our training
set exhibited artist-specific phrasing, we performed two typesof experiment. First, for each artist we held out one test songand trained our model on the remaining songs, repeating thisfor each of the artist's ten songs (inter-artist testing). We werealso interested to see if a more general phrase detection model
could be developed, and so for each test song also trained amodel on every tab which was not by the given test artist (30tabs total, intra-artist testing).
Performance was measured by calculating the precision,
recall, and f-measure of detection of phrase boundaries,
with an exact match required for a 'hit'. The results ofour experiments can be seen in Table II. Inspecting the left
portion of Table II, we see that the total precision of ourmodel in detecting boundaries was 0.73. Recall was lower
than precision, implying that our model was too cautious in
predicting boundaries. In f-measure our boundary detection
performance totals 0.63, a figure comparable with existing
methods [26]. Interestingly, the right portion of Table II reveals
similar performances, indicating that the features we used for
detecting phrases are shared among guitarists, and that a generalmodel for detecting phrase boundaries in symbolic guitar solos
is plausible.
Upon closer inspection of our results, we found the reason
for lower recall in boundary detection was that our model did
not predict a boundary when confronted with short repeated
melodic motifs. These cues are currently not built into ourdetection or generation model, with the knock-on consequencethat our generated solos will not exhibit this behaviour. Building
repetition into our boundary detection and generation models
therefore forms part of our future work.

C. Generation of novel guitar solos
The quality of the generated solos was assessed by theuse of listening tests. We studied the efficaciousness of thethree main attributes of our compositional model: highlighting
chord tones, musical phrasing, and guitar-specific ornaments,

5
TABLE I.

Guitarist

GUITARISTS, THEIR ASSOCIATED ARTISTS AND THE SONGS USED TO TRAIN THE MODEL.

Training tabs

Associated acts
Solo, Cream, Derek & theEric Clapton

Dominos, John Mayall &the Bluesbreakers

Jimi Hendrix

The Jimi Hendrix

Bad Love, Badge, Crossroads, Cocaine, Hide Away, Layla, Nobody Knows You
When You're Down and Out, Old Love, Sunshine of Your Love, White Room, Layla
All Along the Watchtower, Bold as Love, Come On, Crosstown Traffic, Fire, Foxy
lady, Hey Joe, Spanish castle magic, Stone Free, The Wind Cries Mary

Experience

Babe I'm Gonna Leave You, Communication Breakdown, Good Times Bad Times,
Jimmy Page

Led ZeppelinHouses of the Holy, Rock and Roll, Stairway to Heaven, Tangerine, The Lemon Song,The Rover, Whole Lotta Love
Civil War, It's So Easy, Knockin' on Heaven's Door, Mr Brownstone, Nightrain,

Slash

November Rain, Paradise City, Sweet Child 0' Mine, Welcome to the jungle, You

Guns N' Roses

Could Be Minetesting the hypothesis that the addition of these attributes willcorrelate positively with listener satisfaction.
Participant and model description: Participants consisted ofsix researchers from the authors' institution. All were male,and had musical experience spanning 0 -25 years.
Models for each of the artists in Table I were trained fromnine of ten songs, with the final song held out for testing. For
each artist, AutoLeadGuitar then generated four solos using
the methods detailed in Subsections III-B -III-G.
To minimise the burden on participants, instead ofgenerating solos corresponding to every possible pair of
attribute activations, we generated four audio clips withincreasing model complexity for each artist. Specifically, weconstructed a baseline model where we set the parametersf3
I0, and 'turned off' phrasing by enforcing thatthe entire solo was comprised of one uninterrupted phrase.
Furthermore, we limited the state space to states whichfeatured no bends, and set the matrices HP and S to be
filled with O. In the next model we set f3
0.5 and I 0.3,
subsequently included phrasing and finally allowed string
bends and other ornaments for the most complex model.
===

Fig. 3. Example AutoLeadGuitar output in traditional (upper) and tab (lower)
notation, trained using Hendrix tabs. Hammer-ons/pull-offs are indicated byslur, slides with a slash and bends with arrows, phrase ends by vibrato.

=

Experimental conditions:

As AutoLeadGuitar exports toMusicXML, the output can be easily synthesized in existing
software packages. Audio was synthesized using GuitarPro witha generic rock guitar timbre and a full backing track. A 20s clip
was taken from the middle of each of the solos with a 2s fadein and out. Presentation order was randomized across model
complexity to prevent any confounding effects of familiarity,and participants were simply asked to rank the solos by eachartist by preference, from 1 (favourite) to 4 (least favourite).
Repeated listens of each clip was permitted.
Videos of the synthesized output (from which the 20s clips
were generated) for our most sophisticated model can beviewed online 2. An example tab produced by our system withall attributes active is also shown in Figure 3.
2https:l!vimeo.com/I00385330, https:llvimeo.com/I00385331, https:llvimeo.
coml100385332, https:llvimeo.comI100385333

603

Results: Ranks for each artist and participant obtained fromour experiments can be seen in Table III. Model sophistication
decreases down rows, with participant rankings shown in therightmost columns. Kendall's T, which counts the number ofcorrectly ranked pairs in a list [27], was used to measure theperformance of each individual ranking and shown in the final
row of each artist.
All rankings in our experiments were non-negative, meaningthat no participant perceived there to be a negative correlation
between model sophistication and enjoyment. In half of all cases
the participants ranked the clips either in complete agreementwith model complexity or swapped a single pair of clips (12124
audio clips with T � 0.67). The solos generated using Jimi
Hendrix tabs as training data appeared to correlate most strongly
with model complexity, with an average T of 0.67 and all but
one listener ranking the most sophisticated model as theirfavourite. Eric Clapton proved the most challenging artist
to rank. An interesting effect we had not anticipated is thatsome participant's ranks more closely correlated with model
sophistication than others (compare T for participants B and6
RAN KINGS AND PERFORMANCE (KENDALL'S T, GRAYTABLE III.

ACKNOWLEDGEMENTS

ROWS) OF GENERATED SOLOS. ABBREVIATIONS: "CHRD." = CHORD TONE
PREFERENCE, "PHRS." = RHTYHMIC PHRASING, "ORN . " = GUITAR-SPECIFIC
ORNAMENTS. CORRECT RAN KINGS ARE IN BOLD.

The authors would like to thank the participants who took
part in the experiment and Graham Percival for his insightfulcomments. This work was supported in part by CREST, JST.

Participant/rank
Artist

Chrd.

Phrs.

Om.

A

Clapton

./

./

./

2

./

./

./

T
Hendrix

./

./
./

./

./

./

./
./
./

./
./

./

T
Slash

./

./

./

T

./

./

./

./

C

1

2

D

2

REFERENCES

E

F

1

2

[1]

2

4

4

4

4

4

4

[2]

0.0

0.67

0.0

0.33

0.67

0.33

[3]

2
3

T
Page

B

3

2

3

2

2

3

4

2

3
1

3

2

4

4

4

4

1 .0

0.67

0.67

0.67

0.67

2

2

2

3

3

3

2

2
3

[4]

4

[5]

2

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2

4

4

4

4

4

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1 .0

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0.33

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0.33

[11]
[l2]
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In future work and with more judges we would like toinvestigate if these agreements are correlated with either genericor guitar-specific musical training.
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C).

=

V.

[14]
[l5]

[16]
[l7]
[18]

[l9]
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[21]

CONCLUSIONS AND FUTURE WORKIn this paper we described AutoLeadGuitar, a system
for generating phrased guitar solos given a chord and key
progression. To gain insight into guitar phrasing, we designed
an HMM for the detection of phrase boundaries which exceeded
0.6 in f -measure and was found to be artist-independent. We
then generated solos in the tablature space, highlighting chordtones, using phrasing and exploiting guitar-specific ornaments.
Listening experiments revealed that adding musical attributes
resulted in an increase in user satisfaction, with significant
differences in model rankings found in all but one case. In
future work, we would like to incorporate repeated melodies
into our phrase detection and generation algorithms and use ahigher-resolution rhythm model.

604

[22]
[23]

[24]

[25]
[26]

[27]

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