composing the remainder himself.” 31
At least in Koenig’s Projekt 1 (1964)l Koenig saw transcription (from computer
output to musical score) as an important part of the process of algorithmic
composition, writing, “Neither the histograms nor the connection algorithm
contains any hints about the envisaged,
‘unfolded’ score, which consists of instructions for dividing the labor of the
production changes mode, that is, the
division into performance parts. The
histogram, unfolded to reveal the individual time and parameter values, has
to be split up into voices.” 24
Hiller, on the other hand, believed
that if the output of the algorithm is
deemed insufficient, then the program
should be modified and the output
regenerated. 34 Several programs that
facilitate algorithmic composition include direct connection to their own
or to third-party computer sound generation.m This connection obviates the
need for transcription and even hinders this arguably fruitful intervention.
Furthermore, such systems allow the
traditional or even conceptual score to
be redundant. Thus algorithmic composition techniques allow a fluid and
unified relationship between macro-structural musical form and micro-structural sound synthesis/processing,
as evidenced again by Xenakis in his
Dynamic Stochastic Synthesis program
Gendy3 (1992). 40
More current examples.
Contemporary (late 20th century) techniques
tend to be hybrids of deterministic
and stochastic approaches. Systems
using techniques from artificial intelligence (AI) and/or linguistics are the
generative-grammarn-based system Bol
Processor software4 and expert systems
(such as Kemal Ebcioglu’s CHORAL11).
Other statistical approaches that use,
say, Hidden Markov Models (as in Jordanous and Smaill20), tend to need a
significant amount of data to train the
system; they therefore rely on and generate pastiche copies of the music of a
particular composer (that must be codi-
Figure 5. Larger result set from simple L-System rules.
2 3 2 1 1 3 2 3 2 3 2 1 1 3 2 1 1 3 2 1 1 3 2 3 2 3 2
1 1 3 2 3 2 3 2 1 1 3 2 3 2 3 2 1 1 3 2 1 1 3 2 1 1 3
2 3 2 3 2 1 1 3 2 1 1 3 2 1 1 3 2 3 2 3 2 1 1 3 2 1 1
3 2 1 1 3 2 3 2 3 2 1 1 3 2 3 2 3 2 1 1 3 2 3 2 3 2 1
l Written to test the rules of serial music but involving random decisions. 23
m Especially modern examples (such as Common Music, Pure Data, and SuperCollider).
n Such systems are generally inspired by Chomsky’s grammar models8 and Lerdahl’s and
Jackendorff’s applications of such approaches
to generative music theory. 28
fied in machine-readable form) or historical style. While naturally significant
to AI research, linguistics, and computer science, such systems tend to be
of limited use to composers writing music in a modern and personal style that
perhaps resists codification because
of its notational and sonic complexity
and, more simply, its lack of sufficient
and stylistically consistent data—the
so-called sparse-data problem. But this
is also to some extent indicative of the
general difficulty of modeling language
and human cognition; the software
codification of the workings of a spoken
language understood by many and reasonably standardized is one thing; the
codification of the quickly developing
and widely divergent field of contemporary music is another thing altogether.
Thus we can witness a division between
composers concerned with creating
new music with personalized systems
and researchers interested in developing systems for machine learning and
AI. The latter may quite understandably
find it more useful to generate music
in well-known styles not only because
there is extant data but also because
familiarity of material simplifies some
aspects of the assessment of results.
Naturally though, more collaboration
between composers and researchers
could lead to fruitful, aesthetically progressive results.
Outside academia. Application of
algorithmic-composition techniques
is not restricted to academia or to the
classical avant garde. Pop/ambient musician Brian Eno (1948–) is known for
his admiration and use of generative
systems in Music for Airports (1978) and
other pieces. Eno was inspired by the
American minimalists, in particular
Steve Reich (1936–) and his tape piece
It’s Gonna Rain (1965). This is not computer music but process music, whereby a system is devised—usually repetitive in the case of the minimalists—and
allowed to run, generating music in the
form of notation or electronic sound.
Eno said about his Discreet Music
(1975), “Since I have always preferred
making plans to executing them, I
have gravitated towards situations and
systems that, once set into operation,
could create music with little or no intervention on my part. That is to say, I
tend towards the roles of planner and
programmer, and then become an audience to the results.” 18
Improvisation systems. Algorithmic
composition techniques are, then,
clearly not limited to music of a certain aesthetic or stylistic persuasion.
Nor are they limited to a completely
fixed view of composition, where all
the pitches and rhythms are set down
in advance. George Lewis’s Voyager
is a work for human improvisors and
“computer-driven, interactive ‘virtual
improvising orchestra.’” 29 Its roots
are, according to Lewis, in the African-American tradition of multi-domi-nance, described by him (borrowing
from Jeff Donaldson) as involving multiple simultaneous structural streams,
these being in the case of Voyager at
“both the logical structure of the software and its performance articulation.” 29 Lewis programmed Voyager in
the Forth language popular with computer musicians in the 1980s. Though
in Voyager the computer is used to
analyze and respond to a human improviser, such input is not essential
for the program to generate music
(via MIDIo). Lewis wrote, “I conceive
a performance of Voyager as multiple
parallel streams of music generation,
emanating from both the computers
and the humans—a nonhierarchical, improvisational, subject-subject
model of discourse, rather than a
stimulus/response setup.” 29 A related
improvisation system, OMAX, from
the Institut de Recherche et Coordina-
o Musical Instrument Digital Interface, or MIDI,
the standard music-industry protocol for in-terconnecting electronic instruments and related devices.