A brief and subjective history of AI technics in music composition

Publish date
Nov. 30, 2020
Feature article

The Early Days

Electronic music i.e., music that employs electronic musical instruments, has been produced since the end of the 19th century. But producing a sound by a computer needed the existence of computers and the earliest known recording of computer music was recorded at Alan Turing's Computing Machine Laboratory in Manchester in 1951. Listen to the Copeland and Long's restauration of the recording and the account of the all-night programming session which led to its creation, and Turing's reaction on hearing it the following morning:

In the late 1940s, Alan Turing noticed that he could produce notes of different pitches by modulating the control of the computer's loudspeaker used to signal the end of a calculation batch. Christopher Strachey used this trick to make the first pieces: the national anthem, a nursery rhyme and Gleen Miller's In the Mood.

By the summer of 1952, Christopher Strachey develop "a complete game of Draughts at a reasonable speed". He was also responsible of the strange love-letters that appear on the notice board of Manchester University’s Computer Department from August 1953.

Strachey's method of generating love letters by computer is to expand a template by substituting randomly chosen words at certain location. Locations belong to certain categories and each category corresponds to a pool of predefined words. The algorithm used by Strachey is as follows:

"You are my" AdjectiveNoun
2. "My" Adjective (optional)Noun Adverb (optional)Verb, "Your" Adjective (optional)Noun
Generate "Your" Adverb, "M.U.C"

Algorithmic control is in italic, locations (placeholders) are underlined and fixed sequence in the output are in bold.

It is the same process that was used in the 18th century by the Musikalisches Würfelspiel to randomly generate music from precomposed options. One of the earliest known examples are the Der allezeit fertige Menuetten– und Polonaisencomponist proposed in 1757 by Johann Philipp Kirnberger. Here is an example by the Kaiser string quartet:

Carl Philipp Emanuel Bach used the same approach in 1758 to propose Einfall, einen doppelten Contrapunct in der Octave von sechs Tacten zu machen, ohne die Regeln davon zu wissen (German for "A method for making six bars of double counterpoint at the octave without knowing the rules"). A perhaps better-known example is that of W. A. Mozart's Musikalisches Würfelspiel K.516f Trio 2 proposed here by Derek Houl:

At the time, people chose at random using a dice. In 1957, a computer was used: Lejaren Hiller, in collaboration with Leonard Issacson, programmed one of the first computers, the ILLIAC at the University of Illinois at Urbana-Champaign, to produce what is considered the first score entirely generated by a computer. Named Illiac Suite, it later became the String Quartet number 4

The piece is a pioneering work for string quartet, corresponding to four experiments. The two composers, professor at the University, explicitly underline the research character of this suite, which they regard as a laboratory guide. The rules of composition and order that define the music of different epochs are transformed in automated algorithmic processes: the first is about the generation of cantus firmi, the second generates four-voice segments with various rules, the third deals with rhythm, dynamics and playing instructions, the fourth explores various stochastic processes:

Whether in musical dice games or in the Illiac Suite, a dialectic emerges between a set of rules driving the structure and form of a piece, and the randomness used to ensure a certain diversity and the exploration of an immense combinatorial game. This dialectic is at work in almost every automated composition system.

At the same time, in France, Iannis Xenakis was also exploring several stochastic processes to generate musical material. He will also mobilize other mathematical notions to design new generative musical processes. In his first book, Musiques formelles(1963; translated in English with three added chapters as Formalized Music – Thought and mathematics in composition, 1972), he previews for instance the application to his work of probability theory (in the pieces Pithoprakta and Achorripsis, 1956-1957), ensemble theory (Herma, 1960-1961) and game theory (Duel, 1959; Stratégie, 1962).

Expert systems and symbolic knowledge representation

We jump in time to the eighties. Expert systems are flourishing. This set of technics takes a logical approach to knowledge representation and inference. The idea is to apply a set of predefined rules to facts to produce a reasoning or answer a question. These systems have been used to generate scores by explaining rules that describe a musical form or the style of a composer. The rules of fugue, or Schenkerian analysis, for example, are used to harmonize in the style of Bach.

A notable example of the rule approach is given by the work of Kemal Ebcioğlu at the end of the eighties. In his PhD thesis work ("An Expert System for Harmonization of Chorales in the Style of J.S. Bach") he develops the CHORAL system based on 3 principles:

  • the encoding of a large amount of knowledge about the desired musical style,
  • the use of constraints both to automatically generate solutions (with backtrack) and to eliminate those that would be unacceptable (so there are rules to evaluate the quality of the result),
  • the use of style-specific heuristics to prioritize the choices of the algorithm when extending a partially created composition.

 Backtracking is a technique used in particular for constraint satisfaction problems, which allows a series of choices to be questioned when these choices lead to an impasse. For example, f we build a musical sequence incrementally, it may happen at some point that we can no longer increment this sequence without violating the constraints we have set ourselves. The idea is then to go back to a previous point of choice and make another choice to develop an alternative. If there are no further possible choices, one has to go back to the previous choice point, and so on until one can develop a complete solution.

Heuristics are practical methods, often relying on incomplete or approximate knowledge, which do not guarantee correct reasoning, but which often produce satisfactory results (and quickly). When the search for an optimal solution is not feasible nor very practical, heuristic methods can be used to speed up the process of finding a suitable solution.

Here is an example of chorale harmonization (first the orignal Bach’s harmonization then teh result produced by CHORAL at 4’42). The concert note skeches the expert system:

Another outstanding example from the same decade is the EMI system “Experiment in Music Intelligence” developed by David Cope at the University of Santa Cruz. David Cope began to develop this system while he was stuck on writing an opera:

“I decided I would just go ahead and work with some of the AI I knew and program something that would produce music in my style. I would say ‘ah, I wouldn’t do that!’ and then go off and do what I would do. So it was kind of a provocateur, something to provoke me into composing.”

The system analyzes the pieces submitted to it as input characterizing a “style”. This analysis is then used to generate new pieces in the same style. The analysis of EMI applied to his own pieces, makes the composer aware of his own idiosyncrasies, of his borrowings and finally leads him to make his writing evolve:

“I looked for signatures of Cope style. I was hearing suddenly Ligeti and not David Cope.” the composer noted, “As Stravinski said, ‘good composers borrow, great composers steal’. This was borrowing, this was not stealing and I wanted to be a real, professional thief. So I had to hide some of that stuff, so I changed my style based on what I was observing through the output [of] Emmy, and that was just great.” +

You can hear many pieces produced by this system. Listen at a A Mazurka in the style of Chopin produced by EMI, and an intermezzo in the manner of Mahler:

Right from the start, David Cope wanted to distribute this music in the classic commercial circuit. They are often co-signed with Emmy, the little name that designates his system. Over the years, the system has evolved with sequels called Alena and Emily Howell who is also a recorded artists.

When David Cope is asked if the computer is creative, he answers: “Oh, there's no doubt about it. Yes, yes, a million times yes. Creativity is easy; awareness, intelligence, that's hard.”

GOFAI versus Numerical Approaches

Subsequent versions of EMI also use learning techniques that blossomed again in the early 2000s. As a mater of fact, throughout the history of computer science, two approaches have confronted each other.

Symbolic reasoning denotes the AI methods based on understandable, explicit and explainable high-level "symbolic" (human-readable) representations of problems. Knowledge and information is often represented by logical predicates. The preceding examples fall more into this category. The term GOFAI ("Good Old-Fashioned Artificial Intelligence") has been given to symbolic AI in the middle of the eigthies.

During this decade, there was a return to the forefront of a range of digital techniques, often inspired by biology but including also advances in statistical sciences and in numerical machine learning. Machine learning relies on numerical representations of the information to be processed. An example of a technique that falls within this domain are artificial neural networks. This technique was already used in the 1960s with the perceptron invented in 1957 by Frank Rosenblatt which allows supervised learning of classifiers. For Instance, a perceptron can be trained to recognize the letters of the alphabet in handwriting. The input of the system is a pixel array containing the letter to be recognized, and the output is the recognized letter. During the learning phase many examples of each letter are presented and the system is adjusted to produce the correct output categorization. Once the training has been completed, a pixel array can be presented containing a letter that is not part of the examples used for training and the system correctly recognizes the letter.

Depending on the time, the dominant paradigm in AI has fluctuated. In the sixties, machine learning was fancy. But at the end of the decade, a famous article put the brakes on this field, showing that perceptrons could not classify anything. This was because its architecture was reduced to a single layer of neurons. It is shown in the following that more complex classes of examples can be recognized by increasing the number of neuron layers. Unfortunately, there was no learning algorithm available at that time to train multi-layered networks.

Such an algorithm appeared in the 1980s but it is still very heavy to implement and it is also realized that to train a multi-layer network, you need a lot, a lot of data.

Machine Learning

At the beginning of the 2000s, the algorithms are still making progress, the machines are much faster and we can access numerous databases of examples as a result of the development of all digital techniques. This favorable conjunction relaunched numerical machine learning techniques and we now encounter the term deep learning at every turn (here “deep” refers to the many layers of the network to be trained).

The contribution of these digital learning techniques is considerable. It allows for example to generate sound directly and not a score (the sound signal being much richer in information, it takes many layers to do this and hours of recorded music to train the network). We have examples of instrument sounds reconstructed by these techniques.






Of course, one can also compose, and there are many examples of Bach's choir. Here is an example of an organ piece produced by a neural network (folk-rnn) and then harmonized by another (DeepBach).

And another example of what can be achieved (with folk-rnn) by training a network on 23,962 Scottish folk songs (from midi type transcriptions).

One challenge faced by machine learning is that of the learning data. For reasons that are rarely discussed, and despite all academic and non-academic researches, the project of interpreting music is a profoundly complex and relational endeavor. Music is a remarkably slippery things, laden with multiple potential meanings, irresolvable questions, and contradictions. Entire subfields of philosophy, art history, and media theory are dedicated to teasing out all the nuances of the unstable relationship between music, emotion and meanings. The same question haunts the domain of images.

The economic stakes are not far away. A company like AIVA thus organized a concert (at the Louvre Abu Dhabi) featuring five short pieces composed by their system and played by a symphony orchestra. Other examples include a piece composed especially for the Luxembourg national holiday in 2017. and an excerpt from an album of Chinese music:

But beware, in fact only the melody is computer generated. The orchestration work, arrangements, etc., are then done by humans. This is also true for a lot of systems that are claiming automatic machine composition, including Schubert's Unfinished Symphony No. 8, finished by a Huawei smartphone.

From automatic composition to musical companionship

Making music automatically with a computer is probably of little interest to a composer (and to the listener). But the techniques mentioned can be used to solve compositional problems or to develop new kind of performances. An example in composition is to produce an interpolation between two rhythms A and B (given at the beginning of the recording)

Another compositional example is to help orchestration problems. The Orchid software family, initiated in Gérard Assayag's RepMus team at IRCAM, proposes an orchestral score that comes as close as possible to a given target sound as input. The latest iteration of the system, Orchidea, developed by Carmine-Emanuele Cella, composer and researcher at the Univ. of Berkeley, gives not only interesting but also useful results.

An original archeos bell and its orchestral imitation
Falling drops and the orchestral results
A roaster and it musical counterpart

Far from a replacement approach, where AI substitutes for human, these new techniques suggest the possibility of a musical companionship.

This is the objective of the OMax family of systems, developed at IRCAM, still in Gérard Assayag's team. OMax and its siblings have been performed all around the world with great performers and numerous videos of public performances and concerts demonstrates the system's abilities. These systems implements agents producing music by creatively navigating a musical memory learned before or during the performance. They offer a wide range of ways to be "composed" or "played".

An example conceived and developed by Georges Bloch with Hervé Sellin at the piano, to which Piaf and Schwartzkopf respond on the theme of The Man I Love. The second part of the video presents a reactive agent that listen to saxophonis Rémi Fox and play back previous musical phrases recorded live during the same performance:

In this other example, a virtual saxophone dialogues with the human saxophonist in real time following the structure of a funk piece. The latest version of OMAX developed by Jérôme Nika, researcher at Ircam, combines a notion of composed "scenario" with reactive listening to drive the navigation through the musical memory. In the three short excerpts, the system responds by reacting to Rémi Fox's saxophone by focusing:

The type of music generation strategy used to co-improvise in the last example, was also used for Lullaby Experience, a project developed by Pascal Dusapin using llulabies collected from the public via the Internet. There is no improvisation here. The system is used to produce material which is then taken up with the composer and integrated with the orchestra.

A last example where AI assists the composer rather than substituting for her or him, is given by La Fabrique des Monstres by Daniel Ghisi. The musical material of the piece is the output of a network of neurons at various stages of its learning on various corpuses. At the beginning of the learning process, the music generated is rudimentary, but as the training progresses, one recognizes more and more typical musical structures. In a poetic mise en abîme, the humanization of Frankenstein's creature is reflected in the learning of the machine:

A remarkable passage is StairwayToOpera which gives a "summary" of great moments typical of operatic arias.

As a temporary conclusion

These examples show that while these techniques can make music that is (often) not very interesting, they can also offer new forms of interaction, open new creative dimensions and renew intriguing and still unresolved questions:

How could emotional music be coming out of a program that had never heard a note, never lived a moment of life, never had any emotions whatsoever? (Douglas Hoffstader)

Jean-Louis Giavitto

CNRS – STMS lab, IRCAM, Sorbonne Université, Ministère de la Culture