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statistical methods and generate-and-test techniques using models of musical proce-
dures. Koenig (Laske
1981
) and Xenakis (
2001
) incorporated more thoroughgoing
stochastic constituents in their composition, with scores and synthesis determined
by multi-level algorithmic processes. So too did Cage in a handful of later multime-
dia works, including HPSCHD, a collaboration with Lejaren Hiller (Pritchett
1993
,
p. 159). Cornock and Edmonds (
1973
) describe the transformations that interactive
tools were already effecting on the roles of both artist and audience, written in the
terminology of “art systems” and multi-agency processes.
In the last quarter of the 20th century, increased computational power has enabled
the wider use of real-time interactive systems (Rowe
1993
, Winkler
1998
) and gen-
erative simulation systems based on physical and biological processes (Berry and
Dahlstedt
2003
, Nierhaus
2009
). Other major touchstones of algorithmic composi-
tion include Karlheinz Essl's
Lexikon Sonate
(
1992
), David Cope's
Experiments in
Musical Intelligence
(
1996
), and George Lewis's
Voyager
(
2007
).
3
Interactive tools for musical creativity have begun to make their way into popu-
lar culture in a number of forms. Brian Eno (
1996
) has historically championed the
cause of generative music through his significant media profile, recently creating
algorithmic soundtracks for games such as Electronic Arts'
Spore
.
4
The transla-
tions of his ideas to the popular iPhone and iPad formats, in interactive ambient
sound apps such as
Bloom
,
5
have attracted popular attention to generative music
systems and this and similar apps underscore a move toward music making with
semi-autonomous music systems.
7.3 The Human-Computer Partnership: Characteristics
and Categories
Interaction with a semi-autonomous music system inhabits an unfamiliar midpoint
on the spectrum of creative relationships. It resides somewhere between tool usage
and human collaboration, inheriting some characteristics of each and adding some
of its own.
In this section, we will explore creative partnerships with generative computa-
tional systems from a number of distinct but related perspectives, with a view to a
fuller appreciation of the potential opportunities and hazards that such partnerships
can yield. These perspectives do not follow a strict progression, but are ordered
based on an attempt to guide the reader intuitively, beginning with abstract princi-
ples and ending with issues of assessment and evaluation. To provide an overview,
we briefly summarise each below, before expanding further in the following sec-
tions.
3
For a more complete history of algorithmic composition, we refer the reader to Collins (
2009
).
