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AutoGuitarTab: Computer-Aided Composition of
Rhythm and Lead Guitar Parts in the Tablature Space
Matt McVicar, Satoru Fukayama, and Masataka Goto

Abstract—We present AutoGuitarTab, a system for generating
realistic guitar tablature given an input symbolic chord and key
sequence. Our system consists of two modules: AutoRhythmGuitar and AutoLeadGuitar. The ﬁrst of these generates rhythm
guitar tablatures which outline the input chord sequence in aparticular style (using Markov chains to ensure playability) andperforms a structural analysis to produce a structurally consistentcomposition. AutoLeadGuitar generates lead guitar parts indistinct musical phrases, guiding the pitch classes towards chordtones and steering the evolution of the rhythmic and melodic intensity according to user preference. Experimentally, we uncovermusician-speciﬁc trends in guitar playing style, and demonstrate
our system’s ability to produce playable, realistic and style-speciﬁctablature using a combination of algorithmic, user-surveyed andexpert evaluation techniques.
Index Terms—Algorithmic composition, computer-aided composition (CAC), symbolic music processing.

I. INTRODUCTION

T

HE generation of music using automated or semi-automated means has an extensive and rich history yet remains
an active area of research [1]. The advent of affordable digitalcomputing has facilitated a rapid increase in research into algorithmic composition systems, driven by scientiﬁc curiosity [2],analysis and replication of style [3], and the potential pedagogical beneﬁts these systems offer [4].
A particularly interesting area of algorithmic music generation is Computer-Aided Composition (CAC), in which the compositional task is shared between computer and human. In thispaper we propose that the ﬁeld of CAC is sufﬁciently maturethat investigation into more specialized models, such as those
geared towards particular instruments, is now timely.
The guitar represents an interesting case study in this regard,
since the design of the instrument limits (by maximum handspan) the playability of certain pieces. Furthermore, the pitch
ranges of guitar strings signiﬁcantly overlap, meaning that
there is no unique way to play a given note. The situation for
polyphonic scores naturally is signiﬁcantly more complex,

Manuscript received October 30, 2014; revised March 04, 2015; accepted
March 21, 2015. Date of publication April 06, 2015; date of current version
May 08, 2015. This work was supported by OngaCREST, CREST, JST. The
associate editor coordinating the review of this manuscript and approving it for
publication was Prof. Woon-Seng Gan.
The authors are with National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8568, Japan (e-mail:mattjamesmcvicar@gmail.com; s.fukayama@aist.go.jp; m.goto@aist.go.jp).
Color versions of one or more of the ﬁgures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identiﬁer 10.1109/TASLP.2015.2419976

Fig. 1. High-level overview of AutoGuitarTab. AutoLeadGuitar processes
are shown in green, AutoRhythmGuitar processes in blue. See Section I-C for
details.

since each constituent note can be played in a number of different positions. This ambiguity has led to the development of
tablature notation (or simply tab, plural tabs), which speciﬁes
the exact position (string and fret number1) to play each note(see Fig. 2).
1frets are logarithmically-spaced ridges on the guitar neck which result indistinct semitone-spaced notes.

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Fig. 2. Traditional notation (upper stave) and guitar tablature (lower stave)
showing the troublesome one-to-many relationship between traditional notationand the guitar fretboard.

In the context of music generation, the non-bijective map
from score to tab implies that compositions generated throughexisting algorithmic means may produce music which is extremely challenging or impossible to play on the guitar, limiting their use for teaching purposes and personal study. To overcome this problem, in this paper we introduce the compositionof music directly in guitar tablature, and place probabilistic constraints on the sequences of notes which our system produces inorder to maintain playability.
A. Motivation
Our primary motivation for studying guitar-speciﬁc CAC isthat output from our system might be used in a compositional
context, in which a composer wishes to write a guitar part for
a new or existing song but lacks sufﬁcient familiarity with the
instrument to do so. Worth noting at this point is that the guitar isa versatile instrument pervasive in many forms of popular music[5], meaning the scope of this work is not particularly limited
by our choice of instrument.
Further motivation for this work is more pedagogical in nature: our system could be used to teach amateur guitarists thepossible creative approaches to playing rhythm or lead guitar
over a given chord or key sequence (see I-B). Finally, froman academic perspective we are interested to see to what extent guitar compositions are player-speciﬁc, and if guitarists canbe identiﬁed by their playing style. Analysis of style and form
therefore constitutes an important ﬁnal impetus of the current
study.
B. Challenges and Proposed Solutions
There are several challenges which must be overcome in thedevelopment of an automatic composition system. Some ofthese are fairly universal in nature, whilst others more speciﬁc
to algorithmic music creation for the guitar. We outline someof these challenges and our proposed solutions below.
Markov-chain-based approaches to algorithmic composition[6] (on which the majority of our approach is based) are popular within the ﬁeld of CAC owing to their low complexity andhighly intuitive nature. However, it is well-known that thesetechniques fail to model long-term behavior beyond the degreeof the model (see II-A). We counter this in our work by embedding structural information in our rhythm guitar compositions, composing guitar solos in distinct musical phrases, and allowing users of our system to specify intensity curves outlining
the desired evolution of the rhythmic and melodic energy of thecomposition.
Capturing the notion of style in guitar playing is another challenge we face in this work. The layout of the instrument (in particular the position options for each note) we believe plays animportant role in this regard. For example, a speciﬁc chord canbe outlined in a large number of ways by a musician using different combinations of rhythms and chord tones. However, on
the guitar there are also different ways of ﬁngering basic chord
shapes with the same pitches, allowing practitioners of the instrument to explore different hand positions and chord shapes
for a set chord.
Indeed, certain hand positions for chords are beneﬁcial forplaying arpeggios, adding additional melodic notes, or simply
for the different timbre they produce [7]. The same can be said
for lead guitar playing, with certain scale shapes facilitating theuse of particular guitar-speciﬁc ornaments such as string bends,
legato phrases or slides. For examples of this aspect of style inexisting pieces, see Fig. 3.
We therefore consider position choice, along with basic
rhythm and pitch selection, to be an element of a guitarist’s
style (both for rhythm and lead playing). We also consider useof muted notes (percussive sounds produced by relaxing thefretting hand and strumming across the strings), string bends(notes which are plucked and subsequently pulled away fromtheir original position resulting in a smooth increase in pitch
due to an increase in string tension), hammer-ons (notes which
are forcefully brought onto the fretboard at frets higher thanexisting notes on the same strings), pull-offs (analogous tohammer-ons), and slides (glissando across frets) to constitute part of what makes a player unique. Note that existingalgorithmic compositional models which generate music in traditional notation are unable to exploit these attractive aspects ofthe guitar, since knowledge of ﬁngering positions is necessary
for determining the applicability of these techniques.
C. System Overview
A graphical outline of our system is presented in Fig. 1. In the
training phase, we collect existing digital tablatures annotatedwith chords and keys, from which pitch-invariant models forrhythm and lead guitar are generated via transposition. Rhythm
guitar measures are stored for further processing; lead guitar
models for phrasing and rhythm are trained from data.
In generation, we begin by conducting an automatic structuralsegmentation of the input chord sequence. This segmentation
is used to cluster rhythm guitar rhythms into distinct groups.
These rhythm clusters, along with the input chord sequence,
pitch model for rhythm guitar, and the structural segmentation
are then fed into the AutoRhythmGuitar module. A digital tablature in MusicXML format is written as output.
For lead guitar, a key sequence input is required. Optional
intensity contours for rhythm and pitch are used in conjunction
with a rhythm, pitch and phrasing model to produce a lead guitar
digital tablature.
MusicXML is used as the output format in this work owing
to its ﬂexibility (guitar ﬁnger positions as well as bends andother guitar-speciﬁc ornaments can be written and read inMusicXML) and portability (ﬁles can easily be imported into
many existing software packages for visualization and audio
synthesis).
D. Contributions and Paper StructureThis paper is an extension of two of our previous publications
on automatic generation of rhythm [8] and lead [9] guitar parts.
The main contributions of the current work are: a new algorithm

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Fig. 3. The approaches ﬁve popular rhythm guitarists have taken to playing over an A major chord. Guitarists and excerpts, from left to right: Eric Clapton,“Wonderful Tonight” (Clapton); Jimi Hendrix, “Stone Free” (Hendrix); Jimmy Page, “Immigrant Song” (Page/Plant); Keith Richards, “The Last Time” (Jagger/
Richards); Slash, “Sweet Child O’ Mine” (Rose/Slash/Stradlin/McKagen/Adler). Notation: slurs indicate hammer-ons/pull-offs, ‘X’ indicates muted notes.

for the detection of segment boundaries based on genetic algorithms, the introduction of polyphony and intensity curves in
AutoLeadGuitar, and a thorough evaluation of our system using
algorithmic tests, user studies and expert consultation.
The remainder of this paper is organized as follows. In
Section II, we discuss the existing literature relevant to thecurrent study. Section III describes our structural segmentation algorithm. An overview of the two main modules ofour system, AutoRhythmGuitar and AutoLeadGuitar, are outlined in Section IV and V. Our algorithms are evaluated in
Section VI, before we conclude in Section VII.
II. RELEVANT WORK
A. Computer-Aided Composition
The ﬁeld of computer-aided composition is a broad and active research topic, making a complete literature review of thesubject beyond the scope of this paper (an excellent overview
can be found in [1]). As such, the current subsection focuses on
the literature most relevant to approach we take in this work:
Markov-chain-based generation of harmony.
Markov initially investigated sequences of time dependent
variables in the context of text analysis in 1913 [1], but it was
not until the mid-twentieth century that Markov chains’ application to algorithmic composition was considered by Olson [10].
In this investigative work, Olson built ﬁrst and second-orderMarkov models of pitch and rhythm from a set of eleven existingpieces, transposing all songs to a common key. The ﬁrst system
to use Markov chains for composition was developed by Hiller
and Isaacson [2], whose Illiac Suite used transition probabilities to assign intervals for each of the constituent instruments. In
slightly later work with Baker [11], transition probabilities for
pitch, note duration, dynamics, number of non-rest notes, andplaying style were learnt from an existing work. Three works
by Xenakis in 1959 [12] also make use of Markovian analysis,used to control systems of sinusoidal sounds, violins, and an entire string orchestra.
In the subsequent decades, Markov chains were exploredby many researchers, including Zaripov and Russell [13],Conklin and Witten [14], and Ponsford et al. [15]–an excellent
overview of the use of Markov chains in the latter half of thetwentieth century can also be found in [16]. More recently,
Pachet [17] has been investigating the use of Markov chains forgeneration of musical melodies, leading to the development of
a number of systems for use by both trained [3] and untrained[18] musicians. A common criticism of Markovian models isthat they fail to model long-term behavior beyond the order ofthe model [19] and can lead to plagiarism by simply replicating
high-probability state sequences, especially with high ordermodels. Pachet and his collaborators acknowledge and counter
this by placing constraints on the maximum permissible orderof sequence which are repeated verbatim [20].
B. Algorithmic Fingering and Arrangement
Following the terminology from Hori et al. [21], we describe
the process of producing the most comfortably playable tablature from a score as computing a guitar fingering. To do so forcomplex pieces is non-trivial even for skilled guitarists, and may
not even be possible. In these cases, minimally altering a piece
to ensure it is playable on the guitar is described as determining
an arrangement of the piece.
Manually producing ﬁngerings and arrangements are sufﬁciently irksome tasks that some researchers have investigatedmethods of automating these procedures. Sayegh [22] introduced an optimal path paradigm to minimize ﬁngering movement, extended by Radicioni et al. [23] to optimize phrase-level,
rather than global, ﬁngering movement. A data-driven approach
to solving the ﬁngering problem was introduced by Radisavljevic and Driessen [24], whose algorithm learns weight costs ofa particular style from fully-labelled tabs. Tuohy and Potter [25]have investigated genetic algorithms for navigating the large
search space of possible ﬁngerings for a given piece.
Hori and collaborators [21] attempted automatic ﬁngeringand arrangement using input-output Hidden Markov Models,
whilst others have attempted to determine playing position
directly from audio [26]–[28], sometimes constrained by aplayer’s proﬁciency [29], or by using video cues as an additional input [30].
III. STRUCTURAL SEGMENTATION
Rhythm guitar compositions feature a large amount of repetition, with speciﬁc parts repeated in different realizations of thesame structural part (verse, chorus etc.)[8]. To produce realistic
rhythm guitar parts, the ﬁrst stage of our processing pipelineis therefore a structural segmentation of the chord sequence auser inputs to our system. We employ a novelty-based approachbased on the work by Foote [31].
A. Self-Similarity Matrix and Novelty Curve Computation
The input to our algorithm is a text ﬁle oflines–one foreach measure in the song. Each line represents the chords in ameasure, with up to one chord symbol per sixteenth note (onechord symbol per beat or even measure can be used to reduce1108

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typographical burden for the user). Letﬁle:be this input chordwith the chord symbols for a measure selected from a chordalphabet . The ﬁrst stage of processing in our method is tocompute an
self-similarity matrix, with
afﬁnity between two measures deﬁned by normalized Hamming
similarity:

where
is an indicator function which returns 1 if and only
if the input statement is true. Any pair of measures in nonequalbinarytime signatures were set to have similarity 0. An(see [31]) is then passed through thecheckerboard matrix
diagonal of , calculating the novelty at measure as:

The resulting novelty curve was subsequently normalized to[0,1] by dividing by maximum value.
B. Segment Boundary Detection
Next, we detect segment boundaries from the novelty curve.
Each measure boundary can either be a boundary or not,meaning there arepossible segment boundary solutions.
We use a Genetic Algorithm (GA) to explore this large solution
space, with a ﬁtness function which assigns high scores tosolutions which attain high novelty scores but are also sparse.
Genetic algorithms have been explored in the past for structuralsegmentation [32], although our approach differs in that ourapproach is applicable to any scenario where a novelty curveis speciﬁed, including the symbolic chord and audio domains(this will be demonstrated in Section VI).
The initial population was set to 1,000 individuals withboundaries in random positions in expectation every 8 downbeats (by setting the probability of a boundary at each point
to be). Let the sum of the novelty function be , and aparticular individual in the population denoted by.
The ﬁtness of an individual given the novelty curve is thendeﬁned by:

Fig. 4. Example of our symbolic structural segmentation algorithm for“Stairway to Heaven” (Page/Plant). Top: downbeat-synchronous chord sequence Self-Similarity Matrix (SSM), with darker shades indicating higher
similarity. Second subﬁgure: novelty curve derived by passing ancheckerboard matrix through the diagonal of the SSM. Final two subﬁgures: theresulting predicted segmentation and ground truth segmentation. Throughout,sections with the same segment label have identical color.

The population was then evolved over 100 generations withsingle point crossover at a random position, and mutation consisting of bit swaps with probability offor each measure.
Individuals for subsequent generations were chosen from the
population using ﬁtness proportionate selection, with the ﬁnal
boundaries chosen as the segmentation with highest ﬁtness at
generation 100.
C. Segment Label Assignment

The ﬁrst of these terms speciﬁes how much of the total novelty ispicked up by and will be maximal when every downbeat is selected as a boundary. The second term in contrast rewards sparse
solutions and is maximal for solutions with no boundaries. Asan additional constraint to avoid solutions with unrealisticallyshort segments, any population members with a segment lessthan 4 downbeats in length were assigned a ﬁtness of 0.

Every segment was initially assigned a unique label. Segment pairs which were either the same length or integer multiple lengths of each other were then considered for merging(assigning the same label).
Let the number of segments identiﬁed by the GA describedabove be . The similarity between each pair of segments wasstored in an upper triangular matrix, with similarity of two segment pairs deﬁned by Hamming similarity(concatenating together copies of the shorter segment to make

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Fig. 5. Rhythm clusters derived using the techniques discussed in Section IV, withclusters and trained on existing digital tablatures by ‘Slash’ (Saul
Hudson). Each of the discovered clusters is shown in a separate subplot, with members of each cluster (example rhythms) collated in rows. Rhythm cluster 1 shows
rhythms with many onsets, rhythm cluster 2 mainly has onsets on the main metrical positions (16th notes 0, 4, 8, 12), cluster 3 has many rests and muted notes,
whilst cluster 4 consists mainly of held notes.

each segment of equal length if necessary). Segment pairs whichwere not integer multiple lengths of each other were assigned asimilarity of 0.
We then inspected, the largest element of , ﬁnding it
at row and column . We assign segments and the samelabel if
exceeds a threshold.
is then set to
0 and we iterate until.
An example of our algorithm for the chords to “Stairway to
Heaven” (Page/Plant) is shown in Fig. 4, where in this example
and throughout the remainder of this paper we set the parameters(recall: size of cherboard matrix),. Here, ouralgorithm has inserted an erroneous boundary at measure 8 andmissed two boundaries near the end of the song (due, we discovered, to constant chord sequence but changing instrumentation),
but is otherwise correct. Most segment labels are correctly identiﬁed. A quantitative analysis of this algorithm is presented in
Section VI-B.
IV. AUTORHYTHMGUITAR
Our rhythm guitar generation module consists of two independent musical aspects: rhythm and pitch. The composition ofthese attributes is guided by the structural analysis conductedin Section III and is outlined in the following subsection. The
reader is referred to our original publication on rhythm guitar
generation [8] for a more thorough overview of this module.
A. AutoRhythmGuitar Rhythm Assignment
In addition to note onsets, rests (periods of silence) andsustained notes, guitarists are able to produce percussive muted
notes by relaxing their fretting hand across the strings andstrumming. Believing these muted notes to be important in
expressive rhythm guitar playing, our rhythm model for rhythm
guitar consists of the following rhythmic note states: {noteonset, held note, rest, muted note}.
As in our previous work, our algorithm proceeds by clustering the training rhythms into
clusters, whereis thenumber of unique segment labels identiﬁed by our segmentation algorithm. The motivation for this clustering is to produce
one rhythmic style for each segment type in the target song–see
Fig. 5 for an example of the kinds of rhythm clusters we discovered. Each segment label is then assigned a cluster of rhythms,
which are pulled from a ‘bag-of-rhythms’ model from theappropriate cluster in generation. Rhythms are repeated acrosssegments with the same label for intra-segment consistency.
B. AutoRhythmGuitar Pitch Assignment
Our next task is to assign pitches to the note onsets determined in IV-A. We achieve this by constructing a bigram modelin the tablature space. Each state in our rhythm guitar pitchmodel consists of a list of (string, fret, bend) triples, with bends
measured in integer number of semitones. We found rhythm guitarists rarely bent notes (only 55 of 8,430 states had at least one
bent string), but included bends for consistency with AutoLeadGuitar’s pitch model (see V-C). An illustrative example of ourstate space for rhythm guitar pitch can be seen by inspecting theﬁrst eighth note in Fig. 3. In our model, this would be described
as:Adopting the convention that strings are numbered from 1(highest-pitch) to 6 (lowest-pitch). We took a biased random
walk over the state space for a chord to assign pitches fromthe model, exploiting the fact that any consecutive pair of states
which occur in our training data are likely to be playable.
States were also transposed up and down the guitar neck toincrease generalization potential of our model. The maximum
transposed fret was chosen such that on no string did any state
exceed the 11th fret, whilst the minimum fret was set such thatthe lowest note was an open string2. States which contained
combinations of open and fretted strings were not transposed
2an ‘open’ string is one which has no fretted ﬁnger positions.

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IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 23, NO. 7, JULY 2015as it was not clear if transposing them would result in playable
hand positions.
As noted in our previous study, sampling initial states for achord often resulted in unrealistic and unplayable jumps aroundthe fretboard. To counter this, we interpolated between an initial
distribution for each chord and a state similarity metric, inspiredby the work of Hori et al. [21]. We opted for a simpler state
similarity in the current work, deﬁning the similarity between
two states by number of frets between the positions of the ﬁrst
ﬁnger.
Speciﬁcally, let the minimum (non-open string) frets in two
statesbeand. The similarity between andwas then calculated via:
(1)
where the normalisation is performed by maximum possible distance (12 frets). We then set the probability distribution of theﬁrst state for a new chord by interpolating between the initial
distribution
for and the similarity between the previousstate
and all states in the state space for :with interpolation weight. By setting close to unity,
we anticipate that our model will output a sequence of states
which are close to each other on the ﬁngerboard, whilst valuesof closer to zero will more faithfully represent the initial state
distributions for each chord. We present an analysis of the parameter , including a method for estimating its value givenlabelled data, in Section VI-C.
V. AUTOLEADGUITAR
This section outlines our method for generating lead guitar
parts. We begin by discussing the importance of phrasing inguitar solos and how this is modelled in AutoLeadGuitar. A
more thorough description of AutoLeadGuitar can be found in
our previous work [9].
A. AutoLeadGuitar Phrase Generation
We believe effective phrasing to be one of the key concepts ingenerating realistic lead guitar compositions. It has been shownthat the composition of music in distinct phrases facilitates perception and analysis by the human auditory system [33], and isessential in conveying expressiveness in music [34].
Many improvisational instrument groups (such as those in
the wind or brass family) are easily able to construct musicalphrases by periodically pausing for breath. This is not the casefor stringed instruments, making tasteful phrasing of improvisedsolos particularly challenging for guitarists. With this in mind
the ﬁrst stage of our lead guitar generation is the generation ofphrase boundaries, which will later be populated with rhythms(Section V-B) and pitches (Section V-C).
Given a set of measures over which to play, we ﬁrst choose
a position within the ﬁrst measure to begin playing, as well asa phrase duration in musical time. This forms the start and endof the ﬁrst phrase. A phrase onset is then selected for the next
phrase, chosen from whatever remains of the current measure
and the next full measure. This process is then repeated until all
solo measures are exhausted.

Phrase starts and durations were modelled probabilisticallyfrom data, necessitating the annotation of solo phrase starts andends for the training data by the authors, which were rounded to
the nearest sixteenth note for the purposes of the current analysis. Phrase start probabilities for each sixteenth note were estimated from histogram counts (with bin width equal to one sixteenth note), whilst phrase durations were modelled using a negative binomial distribution–the discrete analogue of the gamma
distribution, commonly used to model wait times [35].
There exists no closed form for determining the maximum
likelihood solutions to determining the number of trials andstop probability of the negative binomial distribution analytically, prompting us to determine these parameters numerically.
Analytical and musicological analyses of the parameters andare conducted in Section VI.
B. AutoLeadGuitar Rhythm Assignment
Lead guitar rhythms were assigned by constructing a note
onset bigram model, which speciﬁed the probability of an onset
at each point within a phrase.
To generate rhythms for each phrase, we simply set the ﬁrst
onset to be the start of the phrase and took a random walk over
the rhythm model until the phrase was exhausted, extending theﬁnal note to end at the start of the following phrase (as described in Section V-A–see Chapter 3 of [1] for a descriptionof Markov-based rhythm generation). The ﬁnal note of the ﬁnal
phrase was sustained to the end of ﬁnal measure of the solo. We
also added vibrato to the ﬁnal note of each phrase to help easily
identify phrase boundaries in our output.
C. AutoLeadGuitar Pitch Assignment
Pitch states for AutoLeadGuitar consisted of a list of (string,
fret, bend) triples as per Section IV-B–note that we have increased the sophistication of this aspect of our system from ourprevious work to now include polyphony. We noticed that bends
were far more common in lead guitar playing, with 2,884 of
6,179 states containing at least one bent string. All training data
was transposed to a common key, and also transposed up anddown the guitar neck up to a maximum fret of 24. When generating pitches, states were assigned by a random walk over thestate space for a key.
Our previous work revealed that guiding the random walkprocess towards chord tones resulted in a signiﬁcant increase inlistener satisfaction [9]. Guiding the random walk process was
implemented in this ﬁrst study by interpolating between the state
probabilities for a chord, and a uniform distribution which was
zero everywhere except for states which were a chord tone.
To extend this idea to a polyphonic state space, we set thetarget distribution function to be the number of strings whose
pitch was a chord tone, normalized by number of active stringsin the state. For example, a state which contains a C and D notewhen the underlying chord is a C major was assigned a weight of
0.5. The weight was subsequently normalized over all candidate
states to form a probability distribution.
As before, the interpolation weights were chosen to be
if a state occurred as the the ﬁnal onset in a phrase andotherwise, where we suspectin most cases.
Methods for learning and from labelled data are presented
in Section VI.

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Fig. 6. Rhythmic and melodic intensity of the solo to “All Along the Watchtower” (Dylan, produced by Hendrix). Rhythmic intensity is shown in blue,
melodic intensity in gray.

D. Controlling Intensity
In Section III, we conducted a structural analysis before generating rhythm guitar content, facilitating the repetition of tab
content across multiple instances of the same segment label. Our
intuition is that the long-term evolution of lead guitar parts relies much less on repetition of identical content across phrases
and is instead guided by an evolution of ‘intensity’ throughout
a song.
This intensity, we propose, can be controlled by the noteduration and pitch height of the composition. For an example
in an existing work see Fig. 6, where we have plotted therhythmic and melodic intensity through the solo for “All Alongthe Watchtower” (Dylan, produced and performed by Hendrix).
We deﬁned rhythmic intensity simply as the inverse of noteduration, and melodic intensity as MIDI note number. Both ofthese features were smoothed with a moving average of 9 notes
and range-normalized.
Fig. 6 shows that the intensity of a guitar solo can varythroughout a composition. In this lead guitar part, we see that
there can exist periods of negative correlation (near the startof the composition) and positive correlation (nearer the end)
within a solo, suggesting to us that rhythmic and melodic
intensity should be modelled separately.
In a step towards modelling intensity, users of AutoGuitarTab
are able to optionally specify rhythmic and melodic intensity
contours which guide the evolution of the tablatures generated
by AutoLeadGuitar–the details of how these functions modify
our system’s output is outlined in the remainder of the current
subsection.
Rhythmic Intensity: Let the bigram lead rhythm transition
matrix formed Section V-B be denoted.
Guiding the rhythmic intensity of the lead part given an intensity contour can easily be realized by making the matrixafunction of intensity.
Given the previous note onset index
and current intensity, we interpolate between the th row ofand a function which biases towards either sixteenth notes (atpoints of high intensity) or eighth notes (at points of low intensity). In particular, when(high intensity), we interpolate
towards a delta function peaked one sixteenth note away fromthe previous onset, with interpolation weight equal to . When
, we interpolate towards a function which has a single
peak one eighth note away from the previous onset, now withinterpolation weight (). Note that the model is unalteredwhen(neither high nor low intensity).

1111

Melodic Intensity: Melodic intensity was controlled bychoice of ﬁrst note in each phrase. The reason for this is that
pitch choice within phrases is already the result of an interpolation scheme between the state distribution and chord tones (seeSection V-C). Furthermore, we are optimistic that specifying
the ﬁrst note in a phrase is sufﬁcient for being able to control
the overall pitch intensity of a phrase (this will be investigatedin VI-D).
We therefore ranked the MIDI numbers of states for each key(using the highest pitch in cases of polyphonic states) resulting
in a list of sorted states. Given the current intensity
we then set the ﬁrst state of the solo to be.
Both rhythmic and melodic intensities were set to be constantwithin phrases to ensure consistency within motifs. Particularintensity functions
we
explore in this paper include a linear increase in intensity anda positive parabola, featuring high intensity at the start and endof a lead guitar part with a calmer section towards the middle(other more complex intensity models are naturally possible).
The efﬁcacy of our system to model intensity will be evaluatedin Section VI-C.

VI. EVALUATION
Evaluating the performance of algorithmic composition systems is known to be challenging, owing to the subjective nature of music quality [36]. There are however a collection oftechniques which can be used to qualitatively or quantitatively
assess such systems. Three particular examples of evaluation
strategies we explore in this paper are algorithmic evaluation,
non-expert user studies, and detailed expert studies. Each ofthese techniques have beneﬁts and drawbacks, and are in particular ranked by increasing qualitative efﬁcacy but decreasing
quantitative efﬁcacy and scalability.
In this paper we take a balanced approach, using algorithmicmeans to assess our model’s parameters, conducting a listening
test with non-expert3 individuals, and asking a professional guitarist to assess playability and modelling of style.
In the ﬁrst of these, we train models from our data andinspect the parameter values and distributions obtained. Thismethodology has the advantage of being easily scalable andmay give us musicological insight into aspects of style learned
by our system. However, this analysis gives us no idea ofto what extent our system successfully produces ‘realistic’music. To research this problem we conduct a medium-scale
listening experiment with musical but non-expert listeners in
Section VI-E, asking a number of participants to identify whichof a pair of audio clips was human-generated and which wascomposed algorithmically.
Note however that these non-guitarists are unlikely to beable to read guitar tablature, or be familiar enough with popular music guitarists to identify nuances in stylistic playing.
Finally then, to understand to what extent our system produces
playable tablature and captures the style of guitarists, we asked
a professional guitarist to play and record some of the output toour system, and also to try and identify the target guitarist in a
number of stylistic experiments.
3‘non-expert’ in this paper means an individual with no guitar training

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TABLE IGUITARISTS, THEIR ASSOCIATED ARTISTS AND THE SONGS USED TO TRAIN AUTORHYTHMGUITAR AND AUTOLEADGUITAR

The remainder of the current Section is organized as follows.
In Section VI-A we outline the data we used to train our models.
Section VI-B is then concerned with evaluation of our structural segmentation algorithm (for which there are well-established performance strategies). A parameter space analysis isthen conduced in VI-C, before the investigation into intensity(VI-D), non-expert survey (VI-E) and expert analysis of playability and style (VI-F).
A. Data Collection
Digital guitar tabs in GuitarPro format were collected fromthe user-generated content site gprotab.net4. Five popular
guitarists (Eric Clapton, Jimi Hendrix, Jimmy Page, Keith
Richards, Slash) were chosen for analysis, with ten songs
chosen as training data. We chose these guitarists as examplesof iconic guitarists which we believe cover a range of styles
which could be interesting from a pedagogical perspective.
Note that, being data-driven, our system can easily be trainedwith new data if desired by the user.
When more than one tab was available for a song, the mostaccurate or complete tab was chosen (the ﬁrst author was sufﬁciently familiar with the songs in the study that he was able toidentify the most accurate version of each song and also verify
the quality of this user-generated content).
We discovered that Keith Richards very rarely played lead
guitar parts, and that some songs did not feature any lead guitar
parts at all. Additional songs for each of the four remaining
guitarists were therefore obtained in order to ensure a balanced
dataset of ten rhythm and lead parts for each artist–the complete
training set is shown in Table I. These tabs were subsequently
converted to MusicXML to facilitate automated analysis, andannotated with downbeat-synchronized chord, key, structuralsegmentation labels for use in training and evaluation.
Note that our data source contains tens of thousands of GuitarPro ﬁle tablatures, although we have chosen to work with aselect few in this study. The reason for this is that annotatingsome parts of the training data (in particular detailed chords andsolo phrase boundaries) even on this small set required many
hours’ work. We therefore decided to work with a set of around4http://www.guitar-pro.com/en/index.php, http://www.gprotab.net/

50 tracks where we could ensure high-quality data and annotations. Despite this, we found that by creating pitch-invariant
models we actually had many data/training points. For example,
we found that our model contained:• 4,425 rhythm guitar rhythm measures (809 of which wereunique)• 940 unique major chord states, 6,254 state-to-state transitions (both after transposition)• 210 unique lead guitar phrases, consisting of 6,128 individual notes• 2,726 unique lead guitar melodic states, 17,957 state-tostate transitions (after transposition)
Naturally, using more data from our source would lead tomore powerful compositional models. However, we feel that wehave gathered and annotated enough data in the current study
to make models of reasonable quality. One option we plan to
explore in the future is automatic or semi-automatic means ofannotating more data which may allow us to exploit a larger
number of digital tabs, at the cost of data quality.
B. Structural Segmentation
We evaluated our structural segmentation algorithm on two
datasets. First, we evaluated in the symbolic domain on the datafrom Table I, using ground truth chord sequences annotated at
the beat level. This experiment will assess how well our segmentation algorithm performs in the most realistic use case,when a user inputs a chord sequence as per Fig. 1. True chords
and beat structure are used under the assumption that users correctly input them into our system. Next, we evaluated on a standard dataset in the audio domain, estimating chords and downbeat positions using automated methods. Although this is notdirectly applicable to usage in our system, it will give an indication of how well our algorithm performs with imperfect chord
sequences and downbeats estimated from audio.
Structural segmentation performance in both cases was evaluated with the Boundary Detection and Frame Pair Clustering
metrics [37]. Precision, recall andwere computedfor each of these metrics. Since in the symbolic domain there are
no explicit onset points, we required an exact match on structural boundaries for a ‘hit’. The results of these experiments,
broken down by artist, can be seen in the top portion of Table II.

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Fig. 7. Fretting hand movements for ﬁve guitarists (normalized by maximum fretting hand movement of 12 frets), trained from existing performances.
TABLE IISTRUCTURAL SEGMENTATION ALGORITHM PERFORMANCE. ABBREVIATIONS:: PRECISION, :RECALL, :(‘RWCpop’) and the cutting-edge (‘MIREX’). This is to be expected as under these experimental conditions the feature extraction is conducted on audio, which contains information not
explicitly relevant to the harmony (drum signals etc). In Frame
Pair Clustering we achieved anof 0.42 comparedto the cutting-edge of. This can be explained we believe by our
choice of ﬁtness function in the genetic algorithm we outlined in
III-B, which takes into account novelty and position of boundaries but not segment labels.
C. Parameter Space Analysis

Inspecting Table II, we see that our algorithm achieves a goodbalance between precision and recall of boundaries (mean precision 0.56, mean recall 0.63). In Frame Pair Clustering however,we achieved higher precision than recall (mean precision 0.69
compared to mean recall 0.49), suggesting that our algorithm
was too cautious in assigning two segments the same label. Interestingly, performance varied across artists, suggesting certain harmonic structures are easier or more challenging for oursystem to detect than others. Inspecting individual songs, we
discovered our algorithm performed most impressively when
the chord sequence accurately reﬂected the song structure (asexpected). Speciﬁc examples of when this assumption failed
included blues songs (whose repetitive chord sequence failed
to reveal any novelty) and the instrumental sections of songs(which our algorithm was unable to distinguish from sections
with the same underlying chord sequence).
Next, we evaluated on a standard audio dataset to assess if
our symbolic structural segmentation algorithm could be extended to work with audio input and to compare to existing
methods. To facilitate this, we extracted downbeats and chords
from audio for the RWC pop music dataset (RWC-MDB-P-2001
Nos. 1-100, hereafter referred to as ‘RWCpop’) [38], using the
web service Songle [39]. Chords were then downbeat-synchronized and converted to the format required by AutoRhythmGuitar and fed into our structural segmentation algorithm. Evaluation was performed using thelibrary [40] with awindow for Boundary Detection and can be seen in theseventh line of Table II (labelled ‘RWCpop’). We also include
the two best-performing algorithms (ranked by boundary andFrame Pair Clustering) from the 2014 MIREX evaluation for
direct comparison (ﬁnal row of Table II, labelled ‘MIREX’).
Comparing these results to those above, we see lower magnitude Boundary Detection
for both our system

Rhythm Guitar Fretting Hand Movement: The parameter
introduced in IV-B represents how strongly a rhythm guitarist
wishes to minimize fretting hand movement when changing
chords. We approximate by gathering fretting hand movement from existing data when chords change and sampling fromthe resulting distribution. Speciﬁcally, each time a new chord
was presented, we calculated the distance a guitarist moved asa proportion of the maximum movement (12 frets). These distances were then binned into a histogram with leftmost bin edges
at. These counts were subsequently normalized and are shown (per guitarist) in Fig. 7.
From this Figure, it is clear that fretting hand movement between chords is an artist-speciﬁc trait. Speciﬁcally, although all
guitarists’ movements are peaked towards minimal mobility,
the degree to which they move their fretting hand differs. For
example, Keith Richards appears to be the most economical
player, moving a normalized distance of less than 0.1 (corresponding to 1 fret) in over half of all chord changes, whilst Jimi
Hendrix exhibits a fatter-tailed distribution.
It is apparent from Fig. 7 that a single value of is not appropriate for modelling fretting hand movement. Therefore, eachtime a chord change is encountered we sample an alpha value
from the centres of the bins deﬁned above. We tested the significance of the differences between values of for pairs of guitarists and found at least one pair which was signiﬁcantly different () for each guitarist. For , the same test yielded
tail probabilities, indicating higher signiﬁcance. With
more guitarists in future work we are interested to see if the difference between and are signiﬁcant, although the limited
sample size of 4 lead guitarists limits the power of this test in
the current scenario.
Lead Guitar Phrase Parameters: Fig. 8 shows the phrase
onset and duration parameters derived from our data for eachlead guitarist. The former is represented by a histogram overeach sixteenth note, the latter by maximum likelihood estimation of a negative binomial distribution. Again, it is clear fromthese plots that guitarists exhibit individuality with respect1114

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Fig. 8. Maximum likelihood estimates for lead guitar phrase parameters for each training artist. Top row: phrase start duration, in sixteenth note resolution. Lower
row: phrase durations modelled by negative binomial distributions. Bars in the plots in the second row show individual data points (phrase durations), dark curvesindicate the maximum likelihood estimate parameters of the negative binomial distribution for each artist (parameters also shown in legend).

guitarists. Jimmy Page and Slash do however tend to end theirlead phrases on chord tones (). The other guitarists inour dataset ended phrases on chord tones more often than in the
middle of phrases as we expected.

Fig. 9. End of phrase and mid-phrase
guitarists estimated from our dataset.

chord tone preferences for the leadto this aspect of their playing. All guitarists generally favour
phrases to start on eighth notes, but for example Jimi Hendrix
begins solo phrases on the ﬁrst sixteenth note in approximately
1 in every 4 phrases. Eric Clapton on the other hand most often
starts phrases on beat 2.
Regarding phrase duration, the peak for all guitarists is a little
under 1 measure, but the exact shape of the distribution is guitarist-speciﬁc. Phrase durations for Jimmy Page are particularlyinteresting as they seem to have high variance–many phrases
are less than 1 measure long but also a signiﬁcant number are
longer than 3 measures.
Lead Guitar Chord Tone Preference: In the AutoLeadGuitar
system, the parameters and represent preferences for lead
guitar parts selecting a chord tone during phrases ( ) and at theend of phrases ( ). We estimated these parameters by simply
counting the number of times a lead guitarist selected a state
which had at least one chord tone (discriminating between midphrase and end-phrase notes). The normalized counts for each
guitarist can be seen in Fig. 9.
Most striking to us from Fig. 9 was the similarity in andfor Eric Clapton and Jimi Hendrix. These values suggest thatneither of these players use chord tones more frequently on theﬁnal notes of their phrases, counter to our intuition. Jimi Hendrix also generally plays chord tones less frequently than other

D. Intensity
Recall that we allow users of our system to optionally input
intensity curves describing the desired evolution of the rhythmic
and melodic intensity of the generated solos. To assess the effectthese curves had on the output to our system, we therefore generated 10 guitar solos for a ﬁxed song (‘Stairway to Heaven’,
Page/Plant) with no intensity curve speciﬁed, and 10 with alinear increase in rhythmic intensity and parabolic function of
melodic intensity. We then calculated the intensities of the resulting solos and plotted them in Fig. 10.
From this Figure, we see no clear patterns in the top two subﬁgures. However, the bottom subﬁgures show that by setting intensity contours (linear increase in rhythmic intensity, parabolic
function of melodic intensity) we were indeed able to steer the
global properties of the solo towards user preference. We ﬁnd
it especially interesting that the melodic intensity can be controlled so effectively simply by specifying the start note of eachphrase–this tells us that guitarists tend to play each phrase in aparticular pitch range. Videos demonstrating our ability to control intensity are available on our YouTube page.5.
E. Non-Expert Listener Tests
A Turing test was conducted with amateur musicians working
in the ﬁeld of music information retrieval to assess the abilityof our system to generate realistic guitar parts. Six participants
were presented with two pairs of audio clips per artist and AutoGuitarTab module, one of which came from the training data
and the other from our system.
Audio was synthesized using GuitarPro with a multi-instrument backing track (to give musical context) with all guitar parts
other than the part of interest removed. Clip length, start and5https://youtube.com/channel/UCdQYTz0-qiHq4UCuQVnJp4g

MCVICAR et al.: AUTOGUITARTAB: CAC OF RHYTHM AND LEAD GUITAR PARTS IN THE TABLATURE SPACE

1115

TABLE IV
CONFUSION MATRICES FOR EXPERT IDENTIFICATION OF STYLE

Fig. 10. Melodic and Rhythmic intensities of 20 guitar solos generated by
AutoLeadGuitar. In the top two subﬁgures no intensity curve was speciﬁed,
whilst in the bottom a linear increase in rhythmic increase in intensity and parabolic function of melodic intensity was desired.
TABLE IIITURING TEST PERFORMANCE ACROSS AUTO GUITAR TAB MODULES
AND TRAINING ARTISTS. DATA INDICATE THE NUMBER OF TIMES THE
HUMAN-GENERATED COMPOSITION WAS CORRECTLY IDENTIFIED

end time and guitar tone were standardized across clip pairs andpresentation order was also randomized. Results for this experiment can be seen in Table III.
The second column of Table III shows that our listeners wereable to correctly identify the human rhythm guitarist in just 40
of 60 cases. We ﬁnd this result encouraging, given the difﬁcultyof the task. We tested the hypothesis that the distribution of correct answers came from a binomial distribution with probabilityof success equal to 0.5 (i.e. that the listeners were guessing randomly) and found the resulting -value to be 0.043. This suggests that our algorithm has composed music which is to someextent indistinguishable from human-generated compositions.
For lead guitar parts (third column of Table III) 34/48 guitar
solos were correctly identiﬁed as human-generated, resulting ina -value of 0.015. We attribute the relative difﬁculty of theTuring tests compared to rhythm guitar playing to the improvised nature of guitar solos and their use of medium-term repetition such as repeated 3 or 4 note motifs.
F. Expert Analysis of Style
In our ﬁnal two analyses, we asked an expert (professional
guitarist with more than 10 years teaching experience) to assess

the output of our system. First, we generated rhythm and lead
guitar parts in the style of each guitarist and asked him to try
and identify the source artist, using tablature alone (i.e., no cues
from guitar tone, backing track etc.). Results for these experiments can be seen in Table IV.
From Table IV, we see that 12 of 25 tablatures generated by
AutoRhythmGuitar were correctly classiﬁed by our expert. The
probability of witnessing an event at least this extreme under thenull hypothesis (the expert was assigning guitarists randomly)
was calculated to be less than
, indicating with high conﬁdence that we should reject the null hypothesis and that AutoRhythmGuitar is able to model style. We found it particularly interesting that rhythm guitar parts generated in the styleof Jimi Hendrix were all correctly identiﬁed by our expert. A
demonstration video showing the different styles were are able
to model is available online6.
As per Section VI-E, we noticed that results for Lead guitar
were less impressive. Speciﬁcally, only 2 of 16 AutoLeadGuitar
parts were correctly identiﬁed by our expert. The conclusion
we may draw from this is that either lead guitar playing is notstyle-dependent, or our model has failed to capture whichever
elements of lead guitar playing constitute style.
We were interested to see which features our expert used
to classify rhythm guitar examples, especially for the Hendrix
tracks, which were identiﬁed without error. When prompted, he
responded that he found certain artists easy to identify:“The rhythm tracks were pretty interesting, I found thatsome jumped out instantly as certain guitarists”
Speciﬁcally, he commented that Hendrix “… rarely takes asimple chord and strum approach” and that “often he’s a bit
more rhythmically complex than most the other players” andused these to identify this artist in our tests. In lead playing,
he commented that actually, symbolic music data might not be
sufﬁcient for identifying players:“I think with solos it often isn’t just melodic content thatis the noticeable difference but literally how Clapton plays
his vibrato or how Page plays his bend and repeat phrases
as how I’d identify this”
This difference between the spectrographic and calligraphic
aspects of style has been noted in the literature [43], and is beyond the scope our symbolic music generation model.
6https://www.youtube.com/watch?v=JYL3QLqqclU1116

IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 23, NO. 7, JULY 2015be awkward on a Les Paul and would sound better on possiblya B or G string”). He also noted the lack of a coherent intensity
through the piece (we did not specify an intensity contour in thiscomposition):“It also fizzles out at the end, the crescendo really feels
like its in bars 138-9”
This we feel is excellent evidence that modelling of intensity
is necessary for generating realistic guitar solos. Finally we notethat overview videos are available on our YouTube page, andshow brief example output from our system in Fig. 11.
VII. CONCLUSIONS
Fig. 11. Output from AutoGuitarTab. Top stave: AutoRhythmGuitar output for‘Wild Horses’ (Jagger/Richards) in the style of Jimi Hendrix. Bottom stave:
AutoLeadGuitar output for ‘Stairway to Heaven’ (Page/Plant) in the style of
Eric Clapton. Phrase ends in the bottom ﬁgure are indicated with vibrato.

G. Playability and Individual Composition Analysis
Finally, recall that one of the main motivations for our systemgenerating tablature was to maintain playability. This aspect ofour system was tested by asking our expert to play two tablaturesgenerated by our system–one for rhythm guitar (in the style ofHendrix), the other for lead (in the style of Slash). We also asked
our expert for his detailed opinion on the style and playabilityof these songs. Videos showing the generated content with his
recording overlaid are available online7.
For rhythm guitar, our expert commented that the piece waslacking some of the properties he was expecting from a Hendrix composition. In particular he said it was lacking arpgeggiated chords, pentatonic phrasing during rhythm sections andwalking basslines. He did however mention that some of thechord voicings were very accurate:

In this paper, we introduced a statistical method for guitar
composition and improvisation. Our model composes playable,
structurally-consistent rhythm guitar parts and solos in distinct
phrases which highlight chord tones and were guided in intensity by user input. Altering the training data of our model allowed us to generate artist-speciﬁc models.
Our main contributions in this work were advancements inthe evaluation of AutoRhythmGuitar and AutoLeadGuitar, butalso a new structural segmentation algorithm which is applicable to both symbolic and audio data, and the introduction of
intensity curves which effectively steer the global rhythmic andmelodic properties of lead guitar parts.
In future work, we would like to improve our GA for
Boundary Detection to include segment label information(including more musically-inspired ﬁtness functions), incorporate repeated melodic motifs into our generation system,
and investigate the relative importance of score, microtimings
and guitar tone (choice of instrument, ampliﬁer etc.) on artist
identiﬁcation performance in listening experiments.
ACKNOWLEDGMENT“Some of the ways that the chords were voiced did seemon target, with the thumb over the neck approach on someof the chords.”

We would like to thank the anonymous reviewers for theirinsightful comments.

For overall structure, he felt that there was too much repetition (“It was also a fairly straight forward and repetitive
rhythm, which you don’t really expect from Hendrix”). This interestingly indicates that our assumption of repetition of rhythm
guitar content throughout the piece is not appropriate for thisplayer. He also found the piece to be playable: “From a playing
point of view there wasn’t a lot of issues once I’d got to used tothe thumb over the neck approach”, although naturally he foundsome of the bars more challenging than others (bar number 64 inparticular). Style was modelled better in the lead guitar part according to our expert:[1] S. Nierhaus, Algorithmic Composition. New York, NY, USA:
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“This seemed to have more of the technical and melodic
stylings that I’d expect from a Slash piece. The unison
bends were exactly what I’d expect and the repetitive bends
with a syncopated rhythm.”
However, our experts’ detailed knowledge of Slash’s playing
style revealed that some of the positions would be challenging
given Slash’s instrument of choice: (“The placement for someof the notes were odd (e.g. the intro on a D string) This would
7https://www.youtube.com/watch?v=n9_e8n9h_RU

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246–249.

Matt McVicar received a Master of Science inmathematics in 2008 and Master of Research in complexity sciences in 2009, both from the University ofBristol. Just prior to the completion of his Ph.D. atthe same institution, he was awarded a scholarshipby the USUK Fulbright commission to conduct
research at Columbia University in the city of New
York. After this he worked at the National Institutefor Advanced Industrial Science and Technology inTsukuba, Japan before returning to the University ofBristol as a Research Associate in data science. His
research interests are focused on data-driven applications to Music Information
Retrieval tasks, and in particular the use of freely available internet resources
to solve these problems.

Satoru Fukayama received his Ph.D. degree
in information science and technology from the
University of Tokyo in 2013. He is currently a
Research Scientist at the National Institute of Advanced Industrial Science and Technology (AIST) in
Tsukuba, Japan. His main interests are in automatedcontent generation (music compositions, music
performances and choreographies) with probabilistic
modeling, machine learning and computational musicology. He has received several awards including
the IPSJ Yamashita SIG Research Award from theInformation Processing Society of Japan.

Masataka Goto received the Doctor of Engineering degree from Waseda University in 1998.
He is currently a Prime Senior Researcher andthe Leader of the Media Interaction Group at the
National Institute of Advanced Industrial Scienceand Technology (AIST), Japan. Over the past 23
years, he has published more than 220 papers inrefereed journals and international conferences andhas received 40 awards, including several best paper
awards, best presentation awards, the Tenth Japan
Academy Medal, the Tenth JSPS PRIZE, and theCommendation for Science and Technology by the Minister of Education,
Culture, Sports, Science and Technology (Young Scientists’ Prize). He hasserved as a committee member of over 90 scientiﬁc societies and conferences,including the General Chair of the 10th and 15th International Society forMusic Information Retrieval Conf.s (ISMIR 2009 and 2014). In 2011, as the
Research Director he began a 5-year research project (OngaCREST Project)on music technologies, a project funded by the Japan Science and Technology
Agency (CREST, JST).