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[20210605_stp002]

Programmes by programs
Composing by computer

Concerts may soon feature music written by artificial intelligence

Science & technologyJun 5th 2021 edition
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Jun 3rd 2021

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THESE DAYS, anyone with a computer can be a composer. Sort of. Give a
piece of commercial software such as Magenta, developed by Google,
the first few notes of a song, and it will make something merrily
tuneful out of them. Tuneful, but not sophisticated. At least, that
is the view of Gerhard Widmer of Johannes Kepler University, in Linz,
Austria.

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In Dr Widmer's opinion, "what they create may contain certain
statistical properties. It's not dissonant, but it's not actually
music...It would create a piece that would last three days because it
has no notion of what it wants to do. It doesn't know that things
need an end, a beginning, and something in-between." He thinks he can
do better. He wants to use artificial intelligence to explore how
toying with a listener's expectations affects the perception of
music, and then to employ that knowledge to create software which can
produce something more akin to Beethoven than "Baa Baa Black Sheep".
That means giving computers an ability to perceive subtleties they
cannot currently detect but might, using the latest techniques, be
able to learn. To this end, Dr Widmer is running a project called
"Whither music?"--a title borrowed from a lecture series given at
Harvard University in 1973 by Leonard Bernstein, a celebrated
20th-century composer.

When human beings listen to music, they subconsciously predict what
the next note will be. One trick composers use is to toy with these
expectations--sometimes delivering what is expected and sometimes
deliberately taking an unexpected turn. Performers then enhance that
emotional manipulation by adding expression--for example, by playing a
particular phrase louder or more staccato than the one which came
before. One thing Dr Widmer is doing, therefore, is teaching
computers to copy them.

To this end, he and his colleagues have amassed a huge body of
recordings captured on specially designed instruments, notably the
Bosendorfer 290 SE, a type of concert piano made in the 1980s which
was rigged by the manufacturers with sensors that measure the force
and timings of the pianist's key-pressing with great accuracy. The
jewel of their collection is a set of performances on a 290 SE by
Nikita Magaloff (pictured), a legendary concert pianist and Chopin
expert, of almost all of Chopin's solo piano work. These were
recorded at a series of six concerts which Magaloff gave in Vienna,
shortly before his death in 1992.

The team's software takes data from these and other, humbler
recordings and compares them with the score as written by the
composer. It is looking for mismatches between the two--places, for
instance, where the performer misses the beat by a few milliseconds
or plays a note more forcefully than the score indicates. By
analysing thousands of performances and comparing them with digitised
versions of the composers' scores, the software learns what
performers are choosing to accentuate when they play, and thus what
those performers think is particularly interesting to the audience.

Other algorithms are being taught the rules of composition. "
[Existing software models] take all the past notes that have already
been played and predict the next note, which has nothing to do with
how a human composer would compose," Dr Widmer explains. "Composition
is a planning process that involves structure. We want to create
models that make predictions at several levels simultaneously." The
team are designing and training individual modules for different
elements of music: melody, rhythm, harmony and so on--with the
intention of combining them into a master program that can be trained
on performances and scores in toto.

Once complete, the resulting megabyte maestro will decide not just
which note follows which, but why that should be so and how that note
should be played. "Instead of saying, 'the next note is statistically
likely to be a C', it would say, 'I believe that the next four bars
will feature some kind of IV-I-V harmony [a common type of chord
progression in Western music], because we had a similar pattern in a
similar melodic context earlier in the piece'."

Software of this sort might have applications beyond composition.
Existing "recommender" algorithms struggle to generate musical
playlists that appeal to particular tastes. A recent paper showed
that they are good at suggesting pieces for fans of pop music with
catholic appetites, but not for those who prefer a specific genre,
such as heavy metal or rap. Software that understands musical
expectancy might do a better job. A program which knows what to
listen out for might discover that the music of Skepta or Slayer has
specific types of musical surprises within it, and, on this basis, be
able to recommend new music with similar surprises.

Whether computer software will ever be able to write music that
stands up to comparison with the likes of Chopin or Cream remains to
be seen. Dr Widmer remains sceptical, but it is hard to see why.
Great art is often a product of knowing when to obey the rules and
when to break them. And that is exactly what he is teaching his
machines. #

A version of this article was published online on June 2nd 2021

This article appeared in the Science & technology section of the
print edition under the headline "Programmes by programs"

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