Machine Learning Meets the Maestros

Even if you can't name the tunes, you've probably heard them: from the iconic "dun-dun-dun-dunnnn" opening of Beethoven's Fifth Symphony to the melody of "Ode to Joy," the German composer's symphonies are some of the best known and widely performed in classical music. Just as enthusiasts can recognize stylistic differences between one orchestra's version of Beethoven's hits and another, now machines can, too. A Duke University team has developed a machine learning algorithm that "listens" to multiple performances of the same piece and can tell the difference between, say, the Berlin Philharmonic and the London Symphony Orchestra, based on subtle differences in how they interpret a score. In a study published in a recent issue of the journal Annals of Applied Statistics, the team set the algorithm loose on all nine Beethoven symphonies as performed by 10 different orchestras over nearly eight decades, from a 1939 recording of the NBC Symphony Orchestra conducted by Arturo Toscanini, to Simon Rattle's version with the Berlin Philharmonic in 2016. Although each follows the same fixed score -– the published reference left by Beethoven about how to play the notes -- every orchestra has a slightly different way of turning a score into sounds.