Resultados descriptivos Gráficos:

Let’s return, for a moment, to the concept of the eighteenth-century musical dice games

we discussed in the previous chapter. We can describe the concept of generative music as an expression not of the musical fragments that are manipulated by the rules of the game but of the dice themselves. The core philosophy of generative music is based on the idea of indeterminacy—the introduction of chance into the unfolding of a composition and the randomization of musical content for the purposes of rendering something that is constantly unique. This interactive music system is sometimes also referred to as

“algorithmic composition” or “procedural music,” but the meaning is the same.

Generative music can be a complicated subject, so let’s break down our discussion into these sections:

• The first generative music

• A historical perspective

• An early video game case study

• Modern generative game music The First Generative Music

To find the simplest and earliest form of generative music, we need look no further than the ancient musical instrument we have probably all encountered at one time or another:

the wind chime. Crafted by a skilled artisan, the wind chime is capable of producing a carefully chosen set of musical tones, all with the ability to sound concurrently and produce a pleasing effect in numerous combinations. After fashioning this instrument, the artisan chooses a place to hang it and the wind does the rest. Through the influence of this random factor, the wind chime produces sequences of tones in a completely unpredictable manner. We can listen to the wind chime for a lengthy period before we hear any tone sequence that occurs in the same order and rhythm as any other we have heard before.

This example, though very simple, is a good metaphor for both the operation and the inherent goals of a generative music system in video game design. Generative music in video games is comprised of a collection of preexisting musical components such as melodic phrases, rhythms, and patterns whose content can be influenced by factors based on pure chance, the state of gameplay at any given time, and mathematical rules of probability. At their simplest, these probability rules share characteristics in common with conditional statements such as “if-then.” As a theoretical example, a fairly simple if-then statement might look like the following:

If a piece of music is in the key of G major, and the last note sounded was an F sharp, then the probability of a G natural following this note will be 80 percent, a D natural 15 percent, and an E natural 5 percent.

The software then determines which of the three outcomes will be selected based on this system. These sorts of probability factors determine how instruments play in a

composition characterized by a highly variable nature. The musical work may be a purely MIDI-based construct, in which the instruments exist as a library of sounds.

Otherwise, the composition may be comprised of audio files containing prerecorded musical segments that are triggered based on mathematical algorithms. This system of musical indeterminacy and chance has an interesting history, from which we can learn a great deal about the core philosophy of the generative approach.

A Historical Perspective

While the idea of generative music is relatively new, it owes much to the earlier concept of “chance music” developed by avant-garde composer John Cage. In speaking about his philosophy of music, Cage said, “I have nothing to say, and I am saying it” (1939, 109).

He believed that in removing himself as the final determinant of the shape and form of his music and essentially having nothing of his own to say, he would create something new. Cage was known for determining the shape of many of his compositions using the I Ching method, which involved the tossing of coins and the consulting of charts of divination (Pritchett 1993). Cage said: “I compose music. Yes, but how? I gave up making choices. In their place I put the asking of questions” (1990, 1).

Cage may have established the idea of chance music, but the composer who applied this theory to modern electronic composition came from a much different background.

As a former member of the glam-rock band Roxy Music, Brian Eno had experimented in the 1970s with some chance music techniques involving pairs of tape recorders.

However, it was not until the 1990s that Eno began to explore a process whereby computer software participated directly in the creative process, producing compositions that constantly shifted and evolved using mathematical algorithms based on probability and chance. Eno popularized the term “generative music” as a way to describe music with the ability to continuously and unpredictably generate itself. “I’ve always been lazy I guess,” Eno (1996) said, “So I’ve always wanted to set things in motion that would produce far more than I had predicted.”

Generative music offers the promise of certain specific advantages to video game development teams and publishers. If music is able to continuously generate new variations of itself by virtue of a mathematical algorithm combined with the element of chance, then it should be able to play for long periods while avoiding repetition fatigue.

Also, the generative music system will theoretically produce vast quantities of music without the need for a music budget capable of supporting such a large amount of original content. Writing about the cost of man-hours in the production of video game assets, Dr. Ian Bogost (2012) of the Georgia Institute of Technology points out this practical advantage to procedural generation: “Where aesthetic rationales for procedural approaches hadn’t made much headway, economic imperatives did. The rising costs of AAA game production catalyzed a new interest in procedural methods in game design.”

Nevertheless, Professor Tim Van Geelen of the Avans University of Applied Sciences

offers a few words of caution about generative music: “It remains a largely unproven method. Developers of multimedia have to spend a significant amount of money on many different kinds of specialists to get a product finished” (2008, 101).

An Early Video Game Case Study

While Eno popularized the term “generative music” in the 1990s, one of the earliest uses of such a system in a video game can be found in Peter Langston’s 1985 score for Ballblazer, published by Activision. In Ballblazer, the game chooses from a set of thirty-two melody fragments based on probability calculations. Langston, director of games development for LucasArts and creator of this system, refers to it as “riffology.” Using this system, Langston was able to produce music capable of playing for prolonged periods of time while avoiding repetition, but there were significant drawbacks to the system. “The music generated by this algorithm passes the ‘is it music?’ test,” Langston writes (1986, 5). “However it doesn’t pass the ‘is it interesting music?’ test after the first ten minutes of close listening, because the rhythmic structure and the large scale melodic structure are boring.”

Efforts have been made to advance the art and science of generative music, including software solutions such as PureData, SuperCollider, and Noatikl that attempt to provide composers with better tools for the creation of procedural compositions. Even so, generative game music still faces an uphill battle when attempting to address Langston’s

“is it interesting music?” test. Careful and laborious attention to the rules governing the generative system can address this issue in part, but the inherently random nature of the system makes such a task very difficult.

On the other hand, the importance of solving this problem may sometimes depend on the development team’s conception of the role of music in their project. The team may be happy with the musical content produced by the generative system whether it passes the

“is it interesting music?” test or not. In this case, we should make every effort to create music that surpasses the team’s expectations—music that passes Langston’s simple yet very important test.

Modern Generative Game Music

Since the techniques involved in generative music are still fairly new to the field of video game design, their uses are not particularly widespread. Most developers prefer other methods of music interactivity, perhaps in part because of the complexity involved in designing and implementing a generative music system.

The most prominent example of a generative music system in a modern video game is to be found in Eno’s score for the Spore video game from Electronic Arts. Spore is an innovative video game based around the idea of generative content. Environments, characters, and situations essentially construct themselves using algorithmic calculations.

Because of the generative nature of the game, the developers decided that they would

adopt a generative approach for the musical score. The audio team created a specialized version of the PureData software, and then Eno spent a week working with the team to create a large library of sounds and musical patterns for the generative system. Once the sounds and patterns were assembled, behavioral scripts could be applied to them. These behaviors would allow the musical patterns to adjust themselves to compensate for in-game events, as well as any other musical content that might be occurring simultaneously. Audio engineer Aaron McLeran described the process of creating music in this system as “composing in probabilities” (Kosak 2008).

Other examples of generative music in recent video games are to be found in releases for handheld devices. The more successful uses of generative music in these games fall into the category of “music games” in which the player’s attention is already focused on musical changes, and the player is therefore better able to appreciate the generative system in action. In these games, players’ actions directly influence the progress of the musical score. The game, through the use of the generative music system, creates the illusion of meaningful player participation in the music creation process. These generative musical scores tend to be fairly ambient in nature and usually favor electronic sound palettes.

Conclusion

Interactive music has become popular in recent years in the video game industry, and the odds are good that musical interactivity will continue to be a sought-after commodity.

The various forms of musical interactivity do not each exist in a vacuum, just as linear music does not necessarily need to be the sole methodology for a video game score. All of these techniques can be used by themselves or in conjunction with each other in whatever combinations will have the most satisfying effect. In the end, the most important consideration for a video game composer is the potential of the music to meet the needs of the game and entertain its players.

In chapters 11 and 12, we’ve discussed four different interactive music models and the ways in which they can enable our music to complement the action of a video game.

Creating interactive music can be a fascinating and exciting endeavor for a game composer. In our enthusiasm for the methodology, we should remember to periodically step back and evaluate the music for its own sake, and not for the way in which it utilizes an interactive model. While interactive music can give the player a fun sense of empowerment over the score, this effect will not be sufficient if the interactivity has also negatively impacted the quality of the music. As game composers, musical excellence is always our principal goal. Using both our personal creativity and our sense of good judgment, we can carefully employ interactive techniques that will result in an outstanding video game score.

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