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.