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VIRTUALBAND: INTERACTING WITH STYLISTICALLY CONSISTENTAGENTS

Names should be omitted for double-blind reviewing

Affiliations should be omitted for double-blind reviewing

ABSTRACT

VirtualBand is a multi-agent system dedicated to live

computer-enhanced music performances. VirtualBand en-

ables one or several musicians to interact in real-time with

stylistically plausible virtual agents. The problem addressed

is the generation of virtual agents that each represent the

style of a given musician, while reacting to human play-

ers. The solution we propose is a generation framework

that relies on feature interaction. Virtual agents exploit

a style database, which consists of an audio signal from

which a set of MIR features are extracted. Musical inter-

actions are represented by directed connections between

virtual agents. The connections are specified as database

filters based on MIR features extracted from the agents

audio signal. We claim that such a connection framework

allows to implement meaningful musical interactions and

to produce stylistically consistent musical output. We il-

lustrate this concept through several examples in jazz im-

provisation, beat boxing and interactive mash-ups.

1. INTRODUCTION

Collective improvisation is a group practice in which sev-

eral musicians contribute their part to produce a coherent

musical output. Each musician tpically brings in musical

knowledge, taste, and technical skills, and more generally

his style, which makes him or her unique and recognizable.

However, good improvisations are not only about putting

together the competence of several individuals. Listening

and interacting to each other is also crucial, as it enables

each musician to adapt his or her playing to the global mu-

sical output in terms of rhythm, intensity, harmony, etc.

The combination of individual styles with the definition of

their interaction defines the quality of a music band. Inshort, group improvisation can be seen as interactions be-

tween stylistically consistentagents.

Previous works have attempted to simulate computa-

tionally the behavior of real musicians in a context of mu-

sical group practice. For instance in [5] a MIDI-based

model of an improvisers personality is proposed, to build

a virtual trio system, but no explicit interactions between

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c 2013 International Society for Music Information Retrieval.

virtual agents and real musicians are proposed. Impro-

tek [9] is a system for live improvisation that generates

musical sequences built in real-time from a live source us-

ing feature similarity and concatenative synthesis. Impro-

tek plays along with a musician improvising on harmonic

and beat based music, in the style of this musician, but

interactions with the virtual agents are uniquely based on

similarity measures, thereby limiting the scope of possible

man-machine interactions. [3] describes the Jambot, a sys-

tem that infers in real-time various features from an audio

signal and then produces percussive responses, following

alternatively two behaviors: imitative or intelligent.

In the imitative behavior, the system responds directly to

a user onset by a percussive sound, which is limited both

in terms of interaction and style. In the intelligent behav-

ior, the system bases its output on a global understanding

of the musical context (tempo, meter, etc.), but not directly

from the user input: the system is not really interacting

with the musician. Most importantly, even the style of the

Jambot is not clearly defined. [6] presents Beatback, a sys-

tem that generates a MIDI signal based on rhythmic pat-

terns and using Markov models. Besides the fact that the

sole use of MIDI signals is restrictive musically, interac-tions are limited to rhythmic elements only. In [8], Martin

et al. proposes a system that interacts in real-time with

a human musician, by adapting its behavior both on prior

knowledge (database of musical situations) and parameters

extracted during the current session, but this system forces

the musician to deal with a graphical interface during the

improvisation, which makes the system hardly usable in a

live performance.

In this paper we revisit the problem of musical interac-

tion with stylistically consistent agents by taking a feature-

based MIR approach to the problem. We introduce Virtu-

alBand, a reactive music multi-agent system in which real

musicians can control and interact with virtual musicians

that retain their own style.

A virtual musician is a representation of an actual mu-

sician as a virtual agent. A virtual agent is designed to

play or improvise like the actual musician it represents. To

build the virtual agent, we record the musician in a musical

situation that fits with the targeted improvisation context.

The resulting audio content is stored in a database. This

database is organized in musically relevant chunks (usu-

ally beats and bars). To each chunk is associated a set of

features that are used to define and implement the interac-

tion schemes. During the performance, the virtual agent

uses concatenative synthesis (see [13] for instance) to gen-


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Figure 1. Architecture of VirtualBand. VirtualBand mod-

ules are in the rectangular frame at the bottom. Connec-

tions between agents are represented by red arrows.

erate music streams as seamless concatenations of database

chunks.

The real musicians, those participating in the improvisa-

tion, are also represented by agents, called here real agents.Real agents do not relate to any prerecorded audio, but

rather extract various acoustic and/or MIDI features from

the musical output of the real musician. These features are

used to implement the interactions with the virtual agents.

The interactions between the real and virtual musicians

are implemented in VirtualBand by connections, which are

the core contribution of our approach. A connection de-

fines precisely the interaction scheme between two agents

(real or virtual). Technically, a connection is a directed link

from a master (real or virtual) agent to a slave virtual agent

(see Figure 1). The link is defined by a mapping between

feature values. These mapping in turn are specified as fil-ters that define which database chunks slave agents should

select from their database, in reaction to the analyzed in-

put. In that sense our multi-agent system is an instance of

a reactive system rather than deliberative [7]. Its main po-

tential lies in the seemingly infinite possibilities provided

by feature interaction, as illustrated in this paper.

In Section 1 we describe the main components of Virtu-

alBand. In Section 2 we show how to build a reflexive vir-

tual band from the interaction point of view, in the context

of jazz improvisation, and illustrate the power of feature

interaction on several configurations of the system. Sec-

tion 3 describes applications of VirtualBand in two othermusical contexts: beatboxing and automatic mash-up.

2. SYSTEM DESCRIPTION

The core of VirtualBand system (see Figure 1) consists of:

A clock that establishes the tempo by sending noti-fications at each beat to every agent. We imposed a

fixed tempo in the current version, mainly because

time stretching algorithms are still not compatible

with the real-time constraints of the system.

An audio engine and a mixer to play the outputs ofthe virtual agents.

2.1 Agents

There are two types of agents: human and virtual agents,

representing respectively actual and virtual musicians.

Human agents are responsible for analyzing the input

(audio or MIDI) from the human musician they represent,

and to extract acoustic features on the signal in real-time.

The values of the features will be used as a means of con-

trolling virtual agents via the connections, as explained be-low.

Virtual agents play music from a style database consist-

ing of prerecorded audio content. The music stored in the

database is organized in bars and beats from which are ex-

tracted a set of feature values. These features are means for

real agents to control the virtual agents via connections.

Concretely, a virtual agent computes at every beat the

next audio chunk to play by choosing among the avail-

able audio beats in its database (and possibly transforming

them). To do so, virtual agents apply a succession of filters

to this database, as specified by its connection with other

agents (see below). Once the next chunk to play (beat orbar) has been selected, the agent performs a cross-fade be-

tween the end of the beat currently being played and the

beginning of the selected beat and proceeds.

2.2 Style Databases

A style database represents the musical skills of a musi-

cian in a given musical style/context. Concretely, a style

database consists of an audio signal from which a set of

MIR features is extracted. The construction of such a style

database requires two steps: recording and extraction.

In the recording step, a musician plays so as to fully ex-

press his musical style, i.e. attempts to cover a large rangeof musical situations (e.g., playing with different intensi-

ties, in different moods, staccato or legato, using various

patterns, etc.). Of course, it is difficult to express ones

style exhaustively, so style databases are limited to specific

musical situations, e.g. defined by musical genre such as

Bossa nova, Swing, etc. and a tempo. Note that the subject

ofindividual style capture is still in its infancy and further

studies should refine this concept.

In the feature extraction step, the recorded audio sig-

nal is segmented into bars and beats (in our current imple-

mentation the tempo is given a priori so the segmentation

task is easy), from which various features are extracted,either from the bars or from the beats. The extracted fea-

tures are common MIR features (temporal features, e.g.,

R.M.S., numbers of onsets or rhythmic patterns, and spec-

tral features, e.g., spectral centroid, harmonic to noise ratio

or chroma), but they can be imported from a specific mu-

sical context (e.g., harmony, if the musician played with a

chord grid).

2.3 Connections

A connection between two agents represents a musical in-

teraction between the corresponding musicians. A connec-

tion consists of two agents (a master and a slave), a pair of

features (one for each agent), and a filter that selects from


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the database the best musical contents (bars/beats) to play

depending on the features of each agent (see Figure 1). The

filter can be designed in various ways, and ofers a lot of

flexibility to represent many diverse interaction scenarios.

In general, an interaction is specified by an equation of the

following form:

Consider a Master agentMA that controls a Slave agent

SA. the problem is to select a chunk in SAs database,

that is most appropriate for the context defined by MA.

This selection is performed using 1) a master-feature type

(e.g. spectralCentroid), 2) a slave-feature type (e.g. hit-

Count) and 3) a master-feature-value, provided at time t

(e.g. 0.8). One problem is that the feature types may oper-

ate in different spaces, with different distributions of val-

ues. Therefore, a mapping is needed to map values in the

master feature space to values in the slave feature space.

Such a mapping can be defined by:

SA.f= (MA.f)

The audio chunk in SA is then determined by applyinga succession of filters to the database, until a single value

is found.

For instance, consider a real guitar agent (RG) that con-

trols of virtual drummer (VD). The master feature of the

connection can be the R.M.S. of the guitar, and the slave

feature is the hit-count, i.e., the number of onsets in each

bar, of the virtual drummer. To select the next beat to be

played by the virtual drummer, the filter establishes a lin-

ear mapping from the input RMS value to a target hit count

value, and then selects in the database the audio beat with

the hit count closestto the target hit count.

Formally, such a filter can be specified as:

RG.RMSmatches VD.Hit-Count

2.4 Harmony

In tonal musical contexts, such as standard jazz, musicians

improvise on a chord sequence that is known to all musi-

cians beforehand (e.g. Solar, by Miles Davis). In Virtu-

alBand, harmony is implemented by a specific agent that

represents the chord sequence, and dispatches harmony to

all other agents.

The chord sequence agent (CSA) provides a unique fea-

ture, called CHORD, whose value is the name of the next

chord in the sequence. This agent is connected to every vir-

tual agent that is harmony-dependent, for instance pitched

instruments (piano, guitar, singing voice). For a virtual

agent Vi, the connection is defined as follows: the master

agent is CSA, its master feature is CHORD and the slave

agent is Vi. The slave feature and filter are left undeter-

mined but should be implemented so that the virtual agent

Vi will play something that is harmonically consistent with

the chord returned by the CSA.

For instance, consider a piano comping agent (PCA)

as slave agent. The database consists of various recorded

beats with a single audio chords. Each beat is annotated

with a feature CHORD which represents the chord name.

In this case the slave feature is the chord of PCA

(PCA.CHORD). The filter is defined as:

PCA.CHORD matches CSA.CHORD

The matching is not necessarily strict, but can use sub-

stitution rules, as explained in the next section.

2.5 Reflexive Style Databases and transformations

VirtualBand can also be used in a memoryless mode, i.e.,

without pre-recorded style databases. One motivation for

getting rid of prerecorded material is to avoid canned mu-

sic effects due to the fact that the audience does not know

the content of the style databases. Instead, those databases

can be built on-the-fly, in a manner typical of interactive

systems such as the Continuator [10] or Omax [9]. Reflex-

ive style databases can be used to generate various species

of self-accompaniments such as duos or trios with oneself

(see, e.g. [11]).

Conceptually, reflexive style database do not differ from

pre-recorded ones. Technically, they raise various real-time issues since their segmentation and feature extraction

must be performed in real-time without interfering with the

main VirtualBand loop, which are not discussed in this pa-

per.

More interestingly, reflexive style database raises spar-

sity issues. Because the database contains only what the

musician has played so far, its size will typically be much

smaller than that of pre-recorded databases. As a conse-

quence, the system has much less possibilities for genera-

tion. This is particularly visible in contexts using predeter-

mined chord grids. In principle, the system requires a long

feeding phase, to accumulate at least one chunk for everychord of the sequence, before it can start playing back rele-

vant audio material. Such a feeding phase is usually boring

for the musician as well as for the audience.

In order to reduce feeding, we propose to expand auto-

matically style databases by using audio transformations,

such as transpositions, as well as explicit musical knowl-

edge. Pitch Shifting is an effect that shifts up or down an

audio signal pitch-wise ( [12]). In VirtualBand, the au-

dio chunks of a database are pitch-shifted so that the trans-

posed bars can be used in new harmonic situations.

We use so-called substitution rules to further expand

the style database. Substitution rules consist in replacing achord by another chord with an equivalent harmonic func-

tion. This operation is widely used in Jazz to bring variety

to standard pieces (see [1]). VirtualBand uses chord sub-

stitutions to play in a certain harmonic context audio that

was recorded in another harmonic context, provided the

two harmonic contexts may be substituted.

By combining transpositions and substitutions, the feed-

ing phase can be drastically reduced. The three examples

of Figure 2 show how to harmonize the song Solar from

a small number of input chords, using transpositions (a),

substitutions (b), and a combination of both (c). The in-

put chords (highlighted) generate the remaining chords of

Solar (marked in the same color). Without substitutions

or transpositions, 12 input chords would be required in the


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Figure 2. Illustration of the feeding phase reduction using

(a) transpositions, (b) substitutions and (c) a combination

of both.

feeding phase. In contrast, (a) requires 5 input chords, (b)

requires 8, and (c) only 2. For instance in (c), the input

chord G min 7can be substituted for C 7 using substitution

G min 7 : C 7, but also F min 7 using a 1 tone downard

transposition, and Bb 7 with a combination of these two

operations.

3. EXAMPLES

A typical jazz guitar combo is a formation where a gui-

tarist improvises melodies on an harmonic grid, while the

two others accompany with chords and bass (i.e., lower

notes of the guitar). Each of these roles, melody, chords

and bass, represent different guitar playing modes. Dur-

ing an improvisation, guitarists typically shift roles in turn.

When a musician takes the lead (solo), the other guitarists

adapt their behavior so that each mode is always played bysomeone.

The following examples illustrate various configurations

of VirtualBand with one real guitarist and two reflexive vir-

tual agents. In the first and second examples we limit the

formation to a duo, in which the virtual agent is specified

respectively by a spectral centroid feature and by a rhythm

pattern feature extracted from real guitarist. Then, we ex-

tend te example to a guitar trio configuration with the pre-

sented settings. An accompanying web site illustrates all

these configurations with videos of the corresponding per-

formances.

3.1 Spectral centroid based duo

For two jazz guitarists improvising together, a common in-

teraction scheme is question-answer dialogues. In such a

configuration, a guitarist plays a melody, and as soon as

he finishes, the other guitarist plays another melody that

borrows elements from the first one. Typically the answer-

ing melody is in the same pitch range, or shares similar

rhythm patterns with the original melody. While a guitarist

proposes a melody, the other one either stops playing, or

accompany the first one with, e.g., chord comping.

We can simulate such scenarios with various configura-

tions of VirtualBand based on pitch features of audio con-

tent. A reflexive style database records the incoming audio

Figure 3. Score of a spectral-centroid based duo: a real

agent (RA) plays the melody of Solar. A virtual agent (VA)

matches the spectral centroid of RA, with a one-bar delay.

At bar 11 (Db maj7) VA uses a transposition of bar 5 (F

maj7) of RA to match the spectral centroid of bar 10 of

RA. At bar 8 (Bb 7), VA uses substitution F min 7 : Bb

7 of bar 7 of RA.

of the real guitarist (RG) and extract the spectral centroidof each bar. We chose the spectral centroid as an approxi-

mation of pitch, but more refined descriptors could be used

interchangably. A virtual agent (VG), associated to the

reflexive database, is connected to the real guitarist. The

connection filter specifies that at each bar, the virtual agent

selects in the database the bar with the spectral centroid

closestto the real guitarists one. This can be specified as:

VG.spectralCentroidmatches RG.SpectralCentroid

In such a mode, the system behaves as a self harmo-

nizer: the virtual guitarist follows the spectral centroid ofthe real guitarist with one bar delay (see Figure 3) music

material that will sound like what the guitarist just played.

Another mode consists in doing the opposite, and forc-

ing the agent to play audio that is far away pitch-wise to

the humans input (of course, still satisfying harmonic con-

straints). Such a configuration is easily specified as:

VG.spectralCentroid oppositeTo RG.SpectralCentroid

In this mode, the two outputs (real and virtual guitar)

are more clearly distinct. Of course, it is possible to switch

from one filter to another during the performance, e.g. by

using specific controllers.

3.2 Rhythm-based duo

Question-answer musical dialogues can also be based on

rhythmical similarities. A guitarist plays a rhythm pattern

for a few bars, and the other one responds by playing a sim-

ilar pattern. This can be emulated simply in VirtualBand

by using other pairs of features.

For instance, we can introduce a feature that represents

the rhythm pattern by using a RMS profile. Such a pro-

file is obtained by computing the RMS 12 times per beat

over one bar. The distance between two R.M.S. profiles is

defined as the scalar product of the two sets. This mode

can be specified as:


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VG.RhythmProfile closestTo RG.RhythmProfile

In such a configuration, the musical interaction is of

course very different. Because the rhythm patterns played

by the virtual agent are not identical to the real guitarists,

this mode can lead to fascinating interactions. However, it

requires typically larger size databases, so it is best used

not right away during the session, but after some time,

when the system has accumlated enough rhythm samples

to play back interesting variations.

Here again, another filter can be designed to play an

opposite rhythm pattern. Such an opposite pattern allows

the musician to play with various playing modes, as ex-

plained in the introduction of this section: if the real gui-

tarist plays (and the database records) gentle chords, and

then more melodies, the virtual agent will be able to ac-

company melodies of the guitarist with chords and chords

with melodies.

3.3 Trio

In a guitar trio, each playing mode (melody, chords and

bass) is typically represented by one musician. With Vir-

tualBand, a eal guitarist can be self-accompanied by two

reflexive virtual agents. Both choose and play their audio

beats from the same reflexive database.

Here, in addition to the R.M.S. profile, the reflexive

database extracts theplaying mode from each recorded beat,

among four possible modes: melody, chord, bass and si-

lence. Automatic playing mode idenfitication is described,

e.g. in [2].

Each virtual agent is associated to a unique playing mode

(one to the bass, one to the chords), and agent follows a

mutually exclusive mode, i.e., they play only if the real

guitarist is not playing in the same mode, following the

scheme used in [11]. Furthermore, like in the previous ex-

ample, each agent follows the rhythmical patterns of the

guitarist, with one bar of delay.

Here the musician is free to play as he wants, the virtual

agents adapt their mode and their rhythm. The real musi-

cian can trigger rhythm patterns he previously recorded by

playing similar patterns. For instance, a walking bass, or

comping chords (a lot of notes per bar, regularly spaced)

can be triggered by playing a fast and regular melody. The

guitar trio with oneself follows more or less the rules of astandard guitar trio. A video showing an example of this

trio can be seen in the accompanying web site 1 .

4. OTHER APPLICATIONS

In this section, we describe applications of VirtualBand in

two other musical contexts.

4.1 Reflexive Beatboxing

Beatboxing is a music style where musicians use their mouth

to simulate percussion. Beatboxing also involves hum-

ming, speech or singing (see [14]). Common beatboxers1 Hidden for anonymity.

1 2 3 4

human t---|t-t-|kk--|t--t|Ap ----|----|----|----|Ah ----|----|----|----|

5 6 7 8

human O-O-|O-O-|A-A-|AO--|Ap ----|t-t-|t-t-|t-t-|Ah ----|----|----|----|

9 10 11 12

human tt--|tktk|OAOA|OAOA|Ap kk--|----|----|tktk|Ah ----|AO--|A-A-|----|

13 14 15 16

human tktk|tktk|----|----|Ap tktk|----|----|tktk|Ah ----|OAOA|OAOA|OAOA|

Table 1. Notation of a 16-bar performance of the reflexive

beatboxing system: t and k are percussive sounds, O and

A

are hummed vowels. Ap and Ah are the percussive andhumming virtual agents. Virtual agents follow a mutually

exclusive principle and try to match the humans rhythm

with a one-bar delay.

typically alternate between modes, but some great beat-

boxers are able to play two modes at the same time (e.g.,

percussion and humming).

Beatboxing with VirtualBand aims at augmenting the

performance of a moderately good beatboxer by allow-

ing him or her to play, via reflexive virtual agents, sev-

eral modes at the same time. Using the same settings than

above (3.3), the system records and stores in a reflexivedatabase the incoming audio of the real beatboxer. From

this audio, the database distinguishes between two play-

ing modes: the percussion mode and the humming mode.

Then two virtual agents are connected to this database, one

representing the percussion mode and the other the hum-

ming mode. The classification is performed following a

similar scheme than [2], except for the set of features se-

lected by the classifier (here harmonic-to-noise ratio, spec-

tral centroid and Yin). Following an mutually exclusive

principle, virtual agents play alternatively, depending on

the mode of the real beatbox, and following his or her

rhythmic patterns. Table 1 illustrates a typical session withsuch an augmented beatbox.

4.2 Mash-up

A mash-up is a song or composition created by blend-

ing two or more prerecorded songs, usually by overlaying

a track of one song over the tracks of another (see [4]).

Mash-up usually exploit multi-track songs, i.e., with songs

whose tracks (e.g., drum track, guitar track, voice track)

are available as separated audio files.

A mash-up requires two songs (or more) that are har-

monically compatible, i.e., songs that share moments where

the harmony is similar, or at least superimposable. Finding

two such songs is difficult. We solve this problem in Vir-


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tualBand with the transposition and substitution (see Sec-

tion 2.5), as they enlarge the harmonic possibilities of a

database.

We represent each track of a song by a virtual agent.

The audio of each track is stored in a different database.

Mashing-up with VirtualBand consists in playing a song

where one agent is muted and replaced by the track agent

of another song (for instance the drum track is muted and

replaced by the drum track of another song). The virtual

agent representing the new track is connected to the muted

agent of the original song. Note that the muted agent still

plays with the rest of the group (even if it is not audible),

in order to master the replacing agent in real-time.

We illustrate mash-up with VirtualBand by replacing

the drummer of the band The Police in Roxanne by

another drummer. We use four different drummers: the

drummer of Nirvana in Smell Like Teen Spirit, the

drummer of Pixies in Hey, and Bossa nova and Funk

drums by the jazz drummer Jeff Boudreaux 2 . Each drum-

mer follows the rhythmical patterns of the muted origi-

nal drummer. We can hear in the provided example that

each drummer plays according to the global structure of

the song, i.e., plays breaks and more intensively on chorus

or bridge.

5. CONCLUSION

We have presented VirtualBand, a reactive multi-agent sys-

tem for live musical improvisation involving real and vir-

tual musicians. VirtualBand revists the problem of inter-

acting with stylistically consistent agents by taking a MIR

approach to the problem. Interactions are specified using

features pairs, taken from the vast library of features de-

veloped in MIR. VirtualBand agents are reactive and not

deliberative, i.e. they do not attempt to exhibit autonomy,

self goal making, planning, etc. However, this paper shows

that even with only reactive agents rich interactions can

take place. This effect is mainly due to the complex in-

teractions that may occur between 2 features computed on

various audio signals, even simple ones like the one used in

this paper. We have illustrated the system with several jazz

improvization scenarios, such as a guitar jazz trio played

by a single musician as well as on other types of musical

situations such as mashup and beatboxing.

VirtualBand illustrates the power of feature interaction

to represent complex and meaningful musical interplay oc-

curring in real performance. As such, it only scratches the

surface of the new field of individual style modeling. Fur-

ther work will focus on issues like how to saturate a

style database, or how to predict the emergence of long

term structure from basic low-level feature interaction.

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