INMAMUSYS: Intelligent Multiagent Music System

Miguel Delgado, Waldo Fajardo, Miguel Molina-Solana∗

Department of Computer Science and Artificial Intelligence, Universidad de Granada, Daniel Saucedo Aranda s/n, 18071 Granada, Spain

Abstract

Music generation is a complex task even for human beings. This paper describes a two level competitive/collaborative
multiagent approach for autonomous, non-deterministic, computer music composition. Our aim is to build a high
modular system that composes music on its own by using Experts Systems technology and rule-based systems princi-
ples. To do that, rules issued from musical knowledge are used and emotional inputs from the users are introduced. In
fact, users are not allowed to directly control the composition process. Two main goals are sought after: investigating
relationships between computers and emotions and how the latter can be represented into the former, and developing a
framework for music composition that can be useful for future experiments. The system has been successfully tested
by asking several people to match compositions with suggested emotions.

Key words: Computer music, Music composition, Multiagent systems, Rule-based system

1. Introduction

Surely music is one of the most difficult human dis-
ciplines. It requires creativity, specific knowledge and
eventually, some manual abilities. It is commonly said
that music is something humans do, but something we
do not understand. There are several mechanisms in-
volved in music and unfortunately, many of them are
still unknown. Others are so complex that we cannot
manage them with current computer tools.

On the other hand, Computer Science has suffered a
huge evolution in just a small period of time. However,
despite all the great applications and problems solved
during the last decades, there are many problems that
computers are unable to deal with nowadays.

Among others, we can point fuzzy representation of
abstract concepts and, in general, dealing with feelings
and emotions. In fact, cognitive processes involving
reasoning, knowledge and experience are hardly repre-
sented in a computer and are current hotspots for Artifi-
cial Intelligence.

Music is a good example of applied AI related with
those topics. It has been demonstrated that music com-
position is a hard task even for humans, so using ma-
chines appears as a very interesting and fascinating area

∗Corresponding author
Email addresses: mdelgado@ugr.es (Miguel Delgado),

aragorn@ugr.es (Waldo Fajardo), miguelmolina@ugr.es
(Miguel Molina-Solana)

of research. AI techniques are going to be applied to the
musical domain with the aim of understanding human
musical abilities. Because there are so many challenges
to deal with, a bottom-up approximation is required for
solving all of them in a modular way.

This paper is organized as follows. In Section 2, we
examine some previous works done in the field. Sec-
tion 3 deals with the architecture of the proposed sys-
tem. In Section 4, we discuss design and implementa-
tion of system components. Section 5 presents an evalu-
ation method and summarizes some users’ impressions
about the output; and in the last section, future work to
be done is presented and discussed.

2. Background

Since first computers were developed many people
have tried to apply them to musical tasks.

There are two main classes in which computer mu-
sic projects could be classified: analysis and composi-
tion. The first one consists on extracting information
from the music itself (or the associated data) in order
to learn some rules, or go to a model that describes the
concrete examples. Because this is not the main field of
this paper, so we are not going to go further. However,
[1], and [2] could be reviewed for more information.

Composition is about generating new music from the
rules. In fact, is doing the process in the other way:

Preprint submitted to Expert Systems with Applications August 30, 2016



’from rules to music’ instead of ’from music to rules’.
According to [3], the final objective of most of the com-
positional prototypes is to demonstrate that standard
musical techniques could be handled by computer pro-
gramming, and also to validate generative music theo-
ries.

Initial approaches in algorithmic composition con-
sisted on randomly selecting notes (mainly pitch and
rhythm) with some constraints in order to generate com-
positions. This vision produced limited results but was
a great point of departure for later works.

Experts systems has been widely used to compose
music. Rules concerning pitch, duration and volume
have tried to apprise the knowledge involved in human
composition. [4] introduced an early example of rule
system. Many works on computer music generation are
based on the approach that the composition rules are
specified by the composer. So that, an expert is needed
in order to give all the knowledge. The problem here
is that Music Theory is not as formal as it should be
to be easily represented in a computer. However, [5]
proposed an interesting model to generate music using
grammars.

Another approach to compose music with computers
is to use genetic algorithms. Genetics have been suc-
cessfully applied to several problems with difficulty in
defining the solution process, or where searching a huge
solution space is needed. Composition falls into this
class of tasks, and some works in this direction can be
found in [6] and [7].

Although many advances have been done during last
decades in Computer Music in general and in algorith-
mic composition in particular, it is true that the greatest
moment was when [8] presented EMI Project.

Many prototypes, throughout the years, have demon-
strated that computers algorithms cannot be compared
with human minds. Machines just produce very simple
compositions in a quite mechanical manner.

Computer programs that perform music with proper
stylistic considerations (like a human expert would) are
very scarce and only work for a few examples and in
very specific domains may be found. The main problem
is that algorithms rarely deal with feelings. And music
without emotions is unworthy and not natural.

[9] expressed his conviction that the unique way for
creating a machine whose creations transmit something
to listeners, should start by simulating emotions in com-
puters. [10] addressed this topic (expression in comput-
ers) from a musicological point of view, while [11] pro-
posed a cognitive model for understanding melodies.

This topic is a current hotspot in computer music, and
many groups are working on it right now. [12], [13],

Figure 1: First level architecture

[14], [7], [15] and [16] can be mentioned as some rele-
vant examples. For further information about compos-
ing music with computers, [17] and [18] works should
be reviewed.

In recent years, agent paradigm has become quite
common. Almost everybody is using it for everything.
The reason is that agents are a very powerful way of
implementing distributed AI. Indeed, they can be com-
bined with others tools, such as rule-based systems,
case-based systems or searching algorithms. Actually,
Agent Theory just defines interactions between agents,
not how they are internally built. Because of that,
we can have a multiagent system where agents can be
implemented with just an algorithm, using CBR tech-
niques, rules, genetics... Interesting work related with
multiagent systems can be found in [19] and [20].

3. Architecture

The architecture we propose in this paper is a two-
layer multiagent system. The current application of
multiagent systems in real-time environments is an area
of increasing interest. In general, multiagent systems
are an appropriate approach for solving inherently dis-
tributed problems, whereby clearly different and inde-
pendent processes can be distinguished.

The first level is the competitive one, where agents
(called composers) compete among themselves to be the
one chosen for composing. This layer allows us to make
an initial separation between composition styles. We
think it does not make any sense to have a one-for-all
agent, so a collection of simple agents specialized in
some task is proposed. Each composer announces its
abilities, and the system, according with user inputs, se-
lects the composer that better fits. See Fig. 1.

2



Figure 2: Second level architecture

This agent chooses, between other parameters,
rhythm, number of voices, and instruments to be used
in the compositions. However, the selected composer
only acts as a director, being useless on its own. Going
further with the modularization principle, the composer
agent finally asks some others agents from the second
level for their collaboration in order to get a solution 1.

This second level contains auxiliary agents that col-
laborate between them, so this layer should be under-
stood as a collaborative level. We call these agents voice
generators, because mainly that is their job. See Fig. 2.

In general, there are just three voices in normal com-
positions: melody, accompaniment and harmony. How-
ever, the system presents no limitations in relationships
between agents. A composer agent can employ as many
voices as desired, and in the other hand, several com-
posers could use the same voice generator.

When some intelligence is needed, agents are to be
designed as intelligent rule-based systems. Otherwise,
they just implement a simple algorithm. At the end, we
have a big set of simple agents that together, manage to
find a whole solution for the problem.

4. Design

INMAMUSYS current prototype has several com-
posers that generate music in different ways: from just
a random composer to more elaborate ones where aes-
thetic principles are mainly searched.

1Solution in this context means just a composition that the system
offers as output, without evaluating its quality

An important design principle that is going to guide
our system is creating a tool that can be used by every-
one. Most prototypes present a very complex interface
that only experts can understand, an even for them, it
is very annoying to fill a huge amount of data in order
to get a composition. In fact, when people speak about
music, they do not usually use technical terms such as
granularity, rhythm or tonality; they use emotions, feel-
ings and abstract concepts. Actually, they are not speak-
ing in low level, but in a high level, where technical pa-
rameters do not exist. Keeping that in mind, our aim is
the users ought to be faced with a friendly interface with
only a few questions; and not really difficult ones, just
questions about the wished music style. In other words,
a high level music interface is to be introduced.

In fact, we are integrating into the system itself the
knowledge about how to use tonality, rhythm and in-
struments in order to get, for instance, a sad music.

Until now, this task has been left in the hands of hu-
mans. We propose to go further providing the machine
with the suitable resources it should use to compose in
a certain way. The human does not have to worry about
dozens of parameters and how they should be combined
to get a certain result.

Java has been chosen as programming language due
to its object oriented style and ease of use. jMusic li-
brary is used for music representation and processing.
This library is described by [21].

4.1. Knowledge representation
We have made use of several ways to represent in-

formation. Firstly, because most agents are designed as
rule-based systems, we need to represent some informa-
tion through IF-THEN rules. They are implemented in
XML in order to achieve an universal way of represen-
tation. Fig. 3 shows the XML schema for rules in the
DTD language. Basically, a knowledge base is a set of
several rules. A rule has two parts: IF and THEN. IF
part consists of some attribute-value pairs; and THEN
has a list of consequents, that can be viewed as actions
to be done. We have decided to employ several rules
subsets in order to respect the modularity, and to make
the inference engine’s job easier. Higher performance is
also reached with this approach.

To write code, we use the representation that jMu-
sic API offers us. This is an object oriented one, very
close to normal Western classical music representation.
There are manuals and several examples in jMusic web-
site (http://jmusic.ci.qut.edu.au). As agents are devel-
oped for a concrete musical style or concrete task, there
is obviously a lot of heuristic information about how to
compose coded in each agent. Agents are implemented

3



Figure 3: XML schema in DTD language for representing rules in the
system

with certain parameters (that only an expert knows) in
order to accomplish its goals. This kind of knowl-
edge should be minimized and moved to a unique and
standard repository, in order to obtain an independent
knowledge database. However, many times it is not easy
to code procedural information in a data format, so we
should be aware that much knowledge about the topic is
into the composition process itself.

System output is a sound file in MIDI format. This
representation is preferred rather than others audio for-
mats such as wav or mp3, because it is easier for sym-
bolic manipulation. Even more, MIDI could be easily
converted into the others two (if needed), but the op-
posite is not true. Eventually, a music sheet could be
generated if needed.

4.2. Composition Agents

Composer agents, as we have previously said, are lo-
cated in the first layer of the architecture. They compete
with others in order to be elected for composing. Basi-
cally, all composers are in charge of defining the num-
ber of voices the composition will have, instruments to
be used, the measure and tonality. They also indicate
which second level agents should be used for generating
voices. In other words, composers act as directors. In
current system there are four implemented composers:
Muzak, Dark, Scales and Random.

Muzak Composer generates music in a Muzak2 way.
This kind of music, also called ambient or elevator mu-
sic, could be described as a soft and quiet one. Brian
Eno wrote ”Ambient Music must be able to accommo-
date many levels of listening attention without enforcing
one in particular; it must be as ignorable as it is interest-
ing”. From this point of view, ambient music does not
need to be very elaborate or complex. Its only requisite
is to be pleasant and agreeable to the human ear.

Dark composer aim is to compose music that pro-
vokes fear in listeners. To do that a set of suitable re-

2MUZAK is the name of a company specialized in this kind of
music. People often refer to ambient music in this way.

sources is used. Dissonances and diminished fifth in-
terval (known as tritone) are heavily employed by this
agent. This interval is called diabolus in musica (the
Devil in music) and has been historically avoided. Great
distances between voices and deep bass chords are also
useful resources that are employed.

Scales Composer is quite simple. It just produces
some ascendant and descendent scales in random tonal-
ities and modes. The mixing of several voices doing
the same in different moments, velocities and tones pro-
duces interesting effects.

Finally, we introduce the Random Composer with its
two variants: the differential, and the independent. In
the first one, intervals are randomly generated, so a note
pitch depends on previous note. The other option is to
randomly generate pitches, so a note is independent of
the rest. Durations are also generated with a random
generator. As the reader can imagine, a composer act-
ing this way produces chaotic compositions without any
internal coherence. So that, chaos is the tag that better
defines the music this composer generates.

4.3. Voice generators
Voice Generators are in charge of producing sounds

with different volumes, durations and intonations for ev-
ery melodic line.

Many kinds of voice generators could be imple-
mented, but the main ones fall into any of the following
classes: melody, harmony, accompaniment and drums.
However, the system architecture is flexible enough to
allow any other desired voices. In fact, the architecture
does not care about the semantic meaning of a certain
melodic line.

4.3.1. Harmony generator
Harmony generator makes use of a set of rules that

indicates which tonal movements are allowed. In this
way, chord progressions can be generated. With this,
we have the skeleton of compositions. We can under-
stand this as a Markov chain model. In Fig. 4 we can
see the set of rules in the current implementation, that
correspond with Fux’s counterpoint rules ([22]).

4.3.2. Melody generator
Melody generators produce the melody of composi-

tions. Current design uses a big set of motives which
are one measure long. A motive is randomly selected in
each measure and put into the melody voice. However,
preliminary tests reveal that acting this way, composi-
tions will not be coherent, in the sense that they will
not seem as a unique entity, but rather, like little pieces
stuck together.

4



Figure 4: Rules used by harmony generator to generate chord progres-
sions

The trick here consists in to randomly select (from the
whole set) a subset of motives to be used in each execu-
tion. To have a small number of motives for each exe-
cution makes the composition appear as a whole, giving
the impression of melodic phrases. In fact, there is no
structure at all to be followed, but the repetition over
and over again of similar motives induces the listener to
think so. Employing this simple trick the output seems
to be organized in phrases (but as seen there is no more
than random selection) and the impression is quite real-
istic.

Moreover, due to the fact that the subset is randomly
selected, it is different for each execution. This is an
important point because it produces a great variety be-
tween two compositions, even thought when they are
generated with same inputs.

4.3.3. Drums generator
Drums generators are in charge of generating a con-

tinuous rhythm. There are several composers that can
be classified within this class. They implement different
rhythms: some offers more density, others are lighter;
ones use various sounds (drums, snare, cymbal, hat...),
others just use a simple drum. At the end, it is the first
level agent (the composer) the one in charge of selecting

Figure 5: INMAMUSYS’s user interface

the appropriate Drum Generator (or even none of them)
for each occasion.

4.4. User interface
Our main aim was to develop an easy and simple in-

terface that anyone could use without problems.
Before, we talked about the purpose of implementing

an easy-to-use system. The objetive here is presenting
to the users an interface that anyone can use, even if
they are not experts. To do that, we need to include
within the system all the information related to technical
parameters. This is a great deal because the matching
between emotions and music parameters is not direct,
and to define parameters themselves is definitively not a
trivial task.

The interface (see Fig. 5) is very dynamic and not
fixed because its contents depend on the composers im-
plemented in the system and the capabilities they an-
nounced. The form only contains terms that agents have
declared in some rule. With this, we avoid unexpected
inputs and assure the system will always be able to give
us a valid output. Also, it lets us add, replace, or mod-
ify agents when any problems appear, making changes
easier.

In addition, we have added the possibility of selecting
which instruments should be used in each voice. How-
ever, if we do not specify an instrument, the system
selects one from those it thinks is a better fit. Not all
instrument combinations sound good, and even if they
do, it is possible that they are not compatible with the
style of the melody. These options exist in order to
demonstrate the complexity of the knowledge involved.
Choosing rhythm, instruments, tonality or granularity
are not trivial tasks, because they should be consid-
ered all together, and related with some other concepts.
By allowing selection of instruments, users can test if
they are able to find combinations that really make great
compositions, beating system choices.

4.5. The composition process
In this subsection, the composition process of Muzak

Composer is described to illustrate how a composer runs
5



because the process is quite similar for the rest of com-
posers.

When Muzak composer is selected by the Manager
Agent, it firstly decides the tempo of the composition
attending to user inputs. The mapping between linguis-
tics tags and exact bpm (beats per minute) is done by us-
ing a normal random number generator with given mean
and variance. The means values for each tag have been
statistically obtained from both a set of classical sheet
music and experts’ feedback.

As said before, composer agents are also in charge
of selecting the number of voices to be used, and the
second level agents are responsible for their generation.

To begin with, the harmony generator is executed,
producing a chord progression which is the skeleton of
the composition.

After that, it is the melody generator’s turn. For
this agent, and in the current implementation, a mo-
tive database is decided to be used (as commented be-
fore), as well as employing one of its elements in each
measure. Even thought there is no kind of structure of
phrases, or a grammar to be followed, the global com-
position seems to have an internal structure. This effect
is suggested by frequent motive repetitions.

Next step is to execute the accompaniment generator.
Goal here is to complete the harmonization of the com-
position. Accompaniment generator introduces some
new notes and completes chords. It could just be a note
every measure, an arpeggio or perhaps something more
elaborate.

It is important to indicate that melody and accompa-
niment are generated always in C major. This is not a
handicap and greatly facilitates the task. However, it is
necessary to transpose them to the correct position in
each measure, according to the tonality and the current
grade. Also, the note in the first time of each measure
must be accentuated. It is up to voice generators to as-
sure that.

5. Experiments and evaluation

The evaluation of any musical work is a complex task
and often comes down to individual subjective opinion.
It depends not only on formal aspects but also on some
stylistics ones. Because of that, it is hard to empirically
evaluate music compositions, and therefore it is difficult
to evaluate the effectiveness of a computer music com-
position system.

Many metrics could be developed, but all of them
will fail as at the end, music (understanding the term
as much more than just a chain of sounds) cannot be
reduced to a number.

Due to this difficulty, many authors will typically con-
clude their papers with a vague comment such as “com-
positions generated by the system are quite impressive
and very promising” or “sometimes, melody seems to
be a bit simple and unelaborated; but many, results are
very human like”. However, this is unsatisfactory for
two reasons: first, evaluating the music produced by the
system reveals little about its utility as a compositional
tool; and second, qualitative and subjective evaluation
by the designers of the system reveals little about the
value of the tool to other composers ([3]).

This author affirms that there is an historical malaise
in adopting suitable evaluation procedures for judging
the degree to which the aims have been satisfied. As
we agree with this assertion, we have also proposed an
evaluation method to assess compositions in the same
way that they are usually appraised: through audience
reactions and critical reviews. So that, a short test has
been design to carry out that task.

5.1. Experiments

To begin with, we asked some people for listening
and evaluating some examples generated by INMA-
MUSYS. Most of them could not believe that a machine
was the real author. For us, this is an important point be-
cause capturing the essence of a human’s composition
method is a great deal. This fact makes us also think
that our system could have passed, in some sense, the
Turing test.

The second part of the proposed evaluation method
was more formal and consists in testing what emotions
were induced on listeners when listening to some com-
positions. The objective was to probe whether composi-
tions generated by the system provokes in listeners emo-
tions such as those that guided the composition process
and were given as inputs to the system. This test will
give us a measure of how the system is able to com-
pose music that successfully matches user emotional re-
quests.

Four compositions were generated with different in-
put values. Songs A, B, C and D are respectively gen-
erated by using the inputs worry, happiness, chaos and
worry again (this input is used twice). System mapped
these emotions into the use of Dark, Muzak, Random
and Dark composers. The four examples were presented
to the listeners without giving them information about
the origin of the songs, and the listeners were asked to
select emotions that better fit what they listen. Users
could tick as much tags as they want from the list: sad-
ness, happiness, fear, worry, chaos and indifference.
Twenty people, involving a huge range in musical ex-

6



Figure 6: Results from the test: Feelings that the four tested songs
provoked in listeners

pertise, participated in our experiment by answering the
questions.

Results can be found in Fig. 6. We were really sur-
prised by them because we did not expect such a clear
confirmation, even more when the emotion-composer
matching mechanism is quite primitive. In all the four
cases, the most repeated options is the right one (the
one that was given as input to the system). It is true that
the list of potential options is small, (we would like to
make it bigger the next time), but it is also clear that
differences between some tags are very small, so that it
would have been easy to get some wrong answer.

It is also noticeable, that many people (12 out of 20)
point out that the first song and the last one were very
similar. They are definitively different but these answers
make us think that Dark composer outputs do not cover
the whole area at the solution space that we supposed,
but just a little zone. We think this should be corrected
in the future, because is important to accomplish the ob-
jective that all the compositions from the same inputs
fall in the same area, but it is also crucial to maximize
this area, not just focusing in a point.

To conclude, we would like to comment that an equi-
librated system has been developed: on the one hand,
there is a great variety between different executions;
on the other, we have managed that each composition
sounds as a whole not like several pieces stuck together.
As said, the whole solution space for a tag (e.g. fear)
is not completely covered, but at least we can say that
almost all the system outputs are classified (by humans)
under the right tag.

6. Conclusions and future work

Composing music is a very complex process that in-
volves many disciplines and tasks. In this paper we have
presented a new approach for composing music using

computers. Because there are so many challenges to
deal with, a bottom-up approximation is required for
solving all of them in a modular way. We are inter-
ested in building a framework for successfully compos-
ing music that provokes some feelings in listeners.

In order to achieve this goal, we have proposed here
a two-level architecture that successfully deals with the
complex problem of music composition, so that, a huge
problem can be broken into smaller tasks. This ap-
proach makes use of several agents and rule-based sys-
tems. It also permits that users were provided by an
easy-to-use interface that hides all the complexity of
music composition. Even more, inputs for this inter-
face are emotional inputs from the users, so that we are
able to address the problem of music expressiveness.

Several design decisions that affect the system were
taken and we have explained them. We have focused
in knowledge representation because it is a main topic.
In that section we also describe different agents, as well
as the role they play in the whole system. At the mo-
ment, just four kinds of composers have been devel-
oped: Dark, Muzak, Scales and Random.

Finally, results obtained after evaluating the current
system are showed. This evaluation has been carried out
with an experiment and in a formal way. Even though
the system is in a quite early stage of development, re-
sults are promising enough to encourage us to continue
working with this framework.

6.1. Future work

The current prototype is quite rigid in the way it deals
with user inputs. By now, we are just using classic rule
matching for selecting agents, and that is a poor ap-
proach for dealing with fuzzy concepts, such as emo-
tions and musical terms. So that, including fuzzy logic
in the inference system is probably the first modification
to be done. This change would requiere major modifica-
tions in the inference engine, as well as the definition of
fuzzy domanisn and liguistic tags. However, we think it
would be worth the effort.

Secondly, we would like to develop more composers
in order to get a bigger collection of these kinds of
agents. Our aim is to obtain as much diversity as possi-
ble not only to compare differents algorithms and com-
positional mechanisms, but for implementing and test-
ing new ideas. These ideas could be related with new
theories of musical styles or cognitive processes. Even
more, as the set of composers growths, we will be able
to reproduce a wider range of human emotions.

Another interesting project would be to develop a
module to automatically obtain composition rules. In

7



the current prototype, this knowledge is given by hu-
mans, and coded into the agents. It would be very pow-
erful if the system could analyze a music sheet, then to
extract some rules, and thus to compose according to
them. The system will win a great flexibility with this
ability. Not in vain, this one is the key idea in Cope’s
system, and an interesting hotspot in current data min-
ing. Doing this, the system would be quite complete,
in the sense that it would include both composition and
analysis tasks.

As seen, there is plenty of room for researching in the
computer music area in general and in composition in
particular. At our department, we feel computer music
is a great opportunity for modern Artificial Intelligence.
We have developed this project with the aim of it being
used as a framework for future experiments and works.

We also believe that Expert Systems and Agent The-
ory have many things to say in this field. Composing
music is a huge problem that should be divided into
several tasks, and it definitively needs intelligence to be
done.

Acknowledgements

This research has been partially supported by the
Spanish Ministry of Education and Science under the
project TIN2006-15041-C04-01.

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