of the key feature that distinguishes humans from other
animals is the fact that we are intrinsically musical.
Music is generally associated with the expression of
emotions, but it is also common sense that the intellect
plays an important role in musical activities. The interplay
between these two elements figures in the research agenda
of a variety of scientific fields, including Neuroscience,
Cognitive Sciences and Artificial Intelligence (AI),
to cite but a few. This essay introduces some fundamental
issues of AI and its interplay with music.
From a number of plausible definitions for music, the
one that frequently stands out in musicological research
is the notion that music is an intellectual activity;
that is, the ability to recognise patterns and imagine
them modified by actions. We understand that this ability
is the essence of the human mind: it requires sophisticated
memory mechanisms, involving both conscious manipulations
of concepts and subconscious access to millions of networked
neurological bonds. In this case, it is assumed that
emotional reactions to music arises from some sort of
parts of our brain do different things in response to
the stimuli we hear. Moreover, music is not detected
by our ears alone; for example, music is also "heard"
through the skin of our entire body. The brain's response
to external stimuli, including sound, can be measured
by the activity of the neurons. Two measuring methods
are commonly used: PET (Positron Emission Tomography)
and ERP (Event-Related Potential). PET measures the
brain's activity by scanning the flow of radioactive
material previously injected into the subject's bloodstream.
Despite its efficiency, this method is rather controversial
because the long term side effects of the radioactive
substances to the health of the subject are not entirely
known. ERP uses tiny electrodes placed in contact with
the skull of a person to measure the electrical activity
of the brain. As far as the health of the subject is
concerned, ERP is safer than PET, but the measurement
is different. Whilst PET scans give a clear cross-sectional
indication of the area of the brain where the blood
flow is more intense during the hearing process, ERP
gives only a voltage level vs. time graph of the electrical
activity of the areas of the brain where the electrodes
have been placed.
understanding of the behaviour of the brain when we
engage in any type of musical activity (e.g., playing
an instrument or simply imagining a melody) is merely
the tip of an iceberg. Both measuring methods have brought
to light important issues that have helped researchers
uncover the tip of the iceberg. PET scans have shown
that listening to music and imagining listening to music
activate different parts of the brain and ERP graphs
have been particularly useful to demonstrate that the
brain expects sequences of stimuli that conform to established
circumstances. For instance, if you hear the sentence
"A musician composes the music", the electrical activity
of your brain will tend to run fairly steadily. But
if you hear the sentence "A musician composes the dog",
the activity of your brain will display significant
negative electrical response immediately after the word
human brain seems to respond similarly to musical incongruities.
Such behaviour obviously depends upon one's understanding
of the overall meaning of the language in hand. A number
of enthusiasts believe that we are born "programmed"
to be musical, in the sense that almost no-one should
have difficulties in finding coherence in simple tonal
Intelligence with AI
The understanding of the behaviour of the human brain
is not, however, identical to the understanding of intelligence.
Physical measurements of brain activity may certainly
endorse specific theories of intelligence, but not all
theories seek endorsement of this sort.
of the goals of AI is to gain a better understanding
of intelligence, but not necessarily by studying the
inner functioning of the brain. The methodology of AI
research is largely based upon logics, mathematical
models and computer simulations of intelligent behaviour.
the many disciplines engaged in gaining a better understanding
of intelligence, AI is one of the few that has special
interest in testing its hypotheses in practical day-to-day
situations. The obvious practical benefit of this aspect
of AI is the development of technology to make machines
more intelligent; for example, thanks to AI computers
can play chess and diagnose certain types of diseases
is generally stated that AI as such was "born" in the
late 1940s, when mathematicians began to investigate
whether it would be possible to solve complex logical
problems by automatically performing sequences of simple
logical operations. In fact AI may be traced back far
before computers were available, when mechanical devices
began to perform tasks previously performed only by
the human mind, including some musical tasks.
intelligence synonymous to the ability to perform logical
operations automatically, to play chess or to diagnose
diseases? Answers to such types of questions tend to
be either biased to particular viewpoints or ambiguous.
The problem is that once a machine is capable of performing
such types of activities, we tend to cease to consider
these activities as intelligent. Intelligence will always
be that unknown aspect of the human mind that has not
yet been understood or simulated.
Intelligent Music Machines
Music is without doubt one of the most intriguing activities
of human intelligence. By studying models of this activity,
researchers attempt to decipher the inner mysteries
of both music and intelligence. From a pragmatic point
of view, however, the ultimate goal of Music and AI
research is to make computers behave like skilled musicians.
Skilled musicians should be able to perform highly specialised
tasks such as composition, analysis, improvisation,
playing instruments, etc., but also less specialised
ones such as reading a concert review in the newspaper
and talking to fellow musicians. In this case the music
machine would need to have some basic understanding
of human social issues, such as sorrow and joy. Will
computers ever display such highly sophisticated and
integrated behaviour? More optimistic enthusiasts believe
happens in other areas of AI research, however, computers
have so far been programmed to simulate most specialised
tasks, but fairly independently from each other. Current
research work is now looking for ways to integrate the
ability to perform a variety of such tasks; e.g., mechanisms
from systems for music analysis are aggregated to systems
for composition in order to allow for the computer to
autonomously compose music in the style of analysed
is debatable whether musicians want to believe in the
possibility of an almighty musical machine. Musicians
will keep pushing the definition of musicality away
from automatism for the same reasons that scientists
keep redefining intelligence. Nevertheless, AI is helping
musicians to better operate the technology available
for music making and to formulate new theories of music.
The notion of formal grammars is one of the most popular,
but also controversial, notion that has sprung from
AI research to fertilise the grounds of these flourishing
new theories of music. Formal grammars appeared in the
late 1950s when linguist Noam Chomsky published his
revolutionary book Syntactic Structures. In general,
Chomsky suggested that people are able to speak and
understand a language mostly because they have mastered
its grammar. According to Chomsky, the specification
of a grammar must be based upon mathematical formalism
in order to thoroughly describe its functioning; e.g.,
formal rules for description, generation and transformation
of sentences. A grammar should then manage to characterise
sentences objectively and without guesswork. Chomsky
also believed that it should be possible to define a
universal grammar, applicable to all languages.
study of the relationship between spoken language and
music is as old as the music of the Western culture.
It is therefore not by accident that Music and AI research
has been strongly influenced by linguistics and particularly
by formal grammars. Many musicologists believed that
Chomsky's assumptions could be similarly applied to
music. A substantial amount of work inspired by the
general principles of structural description of sentences
has been produced, including a variety of useful formal
approaches to musical analysis.
composed Entre l'Absurde et le Mystère?
Entre l'Absurde et le Mystère is a piece for
chamber orchestra produced by CAMUS, a computer system
designed by the author a few years ago. CAMUS uses a
type of formal grammar to produce sequences of music
structures (e.g., melodies, chords, clusters, etc.).
The grammar of CAMUS was designed specially to produce
music based upon computer simulations of biological
behaviour using a class of mathematical formalisms called
public warmly applauded its performance by Orquestra
Sinfonica de Porto Alegre (OSPA) in 1995 in Porto Alegre,
Brazil. Roberto Garcia, the guest conductor from Uruguay,
was reluctant to believe that a computer had generated
the piece and generally members of the audience found
that the piece was pleasant. The main question of the
evening was: "Was the piece really composed by a computer?"
question is debatable and has serious ideological implications.
In our point of view, a distinction between author and
meta-author should be made in such cases. The ultimate
authorship of the composition here should be to the
person who designed and/or operated the system. Even
in the case of a program that has the ability to program
itself, someone is behind the design and/or the operation
of the system. Similarly, one would hardly consider
that Pierre Boulez's Polyphonie X, for example, was
composed by the serial system.
Formal grammars are suitable for the description of
the syntactical rules of a language but they do not
guarantee meaningful formations. If one wishes to program
a computer to produce meaningful sentences automatically,
some notion of semantics must be included in the system.
Although semantics is a concept primarily related to
spoken languages, it also applies to music to a certain
often referred to in AI jargon as declarative knowledge,
is a crucial aspect of AI research. A substantial amount
of research work is dedicated to the design of methods
to represent knowledge effectively.
of AI systems require knowledge representation techniques
that provide representational power and modularity.
They must capture the knowledge needed for the system
and provide a framework to assist the systems designer
to easily organise this knowledge.
primary assumption in AI is that mental activity is
mediated by internal representations. Although there
is no consensus as to what these representations actually
are (some regard them as neurophysiological states,
whilst others may define them as symbols or even images).
The traditional approach to AI assumes that intelligent
activity is achieved through:
use of symbols to represent a problem domain
use of these symbols to generate potential solutions
selection of a suitable solution to a problem.
use of an adequate knowledge representation technique
is therefore one of the most important keys for the
design of successful AI systems.
Mathematics and logics undoubtedly play a dominant role
in the formalisation of intelligence for AI research.
But is formalisation the right approach to express intelligent
behaviour? Is it right to distinguish between mind and
body, semantics and syntax, knowledge and abstract representation
great majority of AI work to date assumes that intelligence
can be simulated by encapsulating chunks of data into
static "packets" of information. Intelligent activity
is then performed by an "engine" that picks and combines
appropriate packets of information stored in memory
in order to achieve specific goals. In this case, knowledge
is often classified into two main groups: declarative
(e.g., the semantics of the grammar or the "meaning"
of the packets of information) and procedural (e.g.,
the grammar itself or the how the engine should function).
fact humans store knowledge in a more complex way. Our
brains are stubborn systems which cannot be deconstructed
so neatly. Human intelligence is formed by both conscious
and unconscious elements distributed at different levels
of layers in our mind. Only the conscious ones can be
objectively accessed and manipulated. We seem to not
have access to the levels which do most of our thinking.
When we think, we certainly change our own rules and
rules that change the rules, and so on, but we cannot
change the lower layers; i.e., neurons always function
in the same way.
approaches to AI seek inspiration from this neurophisiological
model. Traditional AI research methods are not necessarily
inspired by neurophysiology but have, nevertheless,
produced fruitful results. Perhaps the most fruitful
of all is the conclusion that there are many types of
intelligence and each has its own characteristics. Music
certainly involves a very distinct type of intelligence
and it is up to music and AI researchers to find the
right approaches to it.
Miranda is a composer and holds a PhD in Music and AI.
He is the editor of the book "Readings in Music and
Artificial Intelligence" by Harwood Academic Publishers
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