Just as IBM's Deep Blue showed the world a computer can play chess as well as a human master, Eduardo Reck Miranda, a researcher for the Sony Computer Science Laboratories Inc., aims to demonstrate a computer program able to compose original music. So far, neural networks have succeeded in imitating distinct musical styles, but truly original compositions have remained elusive. Miranda is tackling that problem with an orchestra of virtual musicians — called agents — that interact to compose original music.

"From the viewpoint of a composer, I can hardly say that our agents are composing music at this stage," Miranda acknowledged. Instead, he said, "the breakthrough in this work is the action of collective machine learning for generative music systems." Rather than modeling specific musical styles or compositional processes, the new approach is a first step toward collective music-making with virtual musicians, each of which learns how to compose and play individually.

The Brazilian-born Miranda composes chamber and electroacoustic pieces, compositions that have won prizes in the Americas, Asia and Europe. Today, at Sony, he specializes in artificial intelligence in music. In his latest book, Composing Music with Computers (Focal Press), Miranda summarizes his AI research, which began with cellular automata and evolved into an "adaptive games" strategy based on artificial-life models.

"Perhaps one of the greatest achievements of artificial intelligence today lies in the construction of machines that can compose music of incredibly high quality," he said.

But AI's achievement is restricted to mimicking the style of existing composers, either with a set of AI rules, or by learning a composer's style with a neural network. In other words, computers can compose a new Bach cantata, but cannot compose anything novel, because their algorithms merely encapsulate a particular style of music.

For a computer to create truly novel compositions, Miranda has turned to artificial life (AL) models — the fodder for what he calls evolutionary musicology. A computer becomes endowed with an adaptive set of music-composition tools and processes. Under the control of an artificial life model the set displays "emergent" behaviors — that is, novel compositions. Miranda has explored this approach with cellular automata, genetic algorithm-like processes he summarized as "adaptive games."

Programming assistance

Miranda began his investigation with musical pieces composed using cellular automata, and created two computer programs to assist in the process — a software synthesizer called ChaoSynth and a generator of musical passages called Camus.

Cellular automata are organized as a set of identical "cells" in a two-dimensional grid. Each has a set of rules to follow, but can only communicate with nearest neighbors. New high-level structures — in this case, music — evolve a pattern through repeated application of each cell's rule set. Over time, a pattern emerges that is translated into a soundscape by Miranda's ChaoSynth.

ChaoSynth uses a granular synthesis method to translate the two-dimensional pattern into a signal that can drive a speaker. By accumulating a sequence of short sound "grains" — typically 10 to 100 microseconds in length — many different cells can contribute to the final sound sample.

"ChaoSynth is a successful system because it can synthesize a large amount of unusual sounds that are not normally found in the real acoustic world, but which nonetheless sound pleasing to the ear," said Miranda.

The ChaoSynth "soundscapes," often accompanied by Miranda on the piano, have won awards for being "musical" despite the unearthly sound of their individual parts. While pleasing when blended in composition, the everyday sounds fit no known category when isolated.

"Up to now, I considered this work more at a theoretical level, as an investigation into the origins of music, rather than as a practical method to actually compose music," Miranda said. "Only now have I started composing using the sounds created by adaptive agents."

Miranda began by equipping his agents with a voice synthesizer, a hearing apparatus, a memory device and an "enacting script" that the system follows when communicating with other agents. The agents compute the parameters of their synthesizer — principally pitch and duration — and play them to each other. The agent's memory stores its repertoire of "songs" and it has the processing capability to extract the pitch of songs it hears so as to compare them with its own repertoire.

A variety of statistical, threshold and reinforcement parameters enables the agents to adapt their behaviors toward the goal of imitation. For instance, just because an agent "hears" a song by tracking its pitch, it cannot exactly imitate the sounds it hears because it is saddled with its own motor-skill limitations. In fact, each agent's motor skills also adapt, so that over time agents become more skillful at imitating songs similar to ones already in its repertoire.

Cover track

Agents keep track of the "popularity" of each of their songs by tracking how many other agents have successfully imitated them. Songs rejected by one imitator but accepted by many other imitators are left alone. But songs that are repeatedly "rejected" by imitators are eventually deleted and replaced with a new song that is a permutation of the rejected song.

For Miranda, the novelty here is that his adaptive games can learn to actively compose music, not just passively generalize existing musical knowledge. If borne out, this hypothesis will lead to realistic musical cultures that can be evolved by furnishing virtual musicians with what he calls "the proper cognitive and physical abilities, combined with appropriate interaction dynamics and adequate environmental conditions."

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