Researchers at the University of Washington have released the latest music resource to aid machine learning.

A research team from the University of Washington has released a new large-scale music dataset, entitled MusicNet, to be used as a resource for machine learning methods in music research. It is believed this research will have significant ramifications for computer generated composition, note prediction and automated music transcription.

The publically available dataset comprises 330 freely-licensed recordings of classical music by ten different composers and written for 11 instruments with over one million annotated ‘labels’. The labels indicate the precise timing of each note in every recording, the instrument that plays each note and the note’s position in the metrical structure of the composition.

“At a high level, we’re interested in what makes music appealing to the ears, how we can better understand composition, or the essence of what makes Bach sound like Bach,” said Sham Kakade in an article published on UW’s website. Kakade is an Associate Professor in UW’s Statistics and Computer Science departments and Adjunct Professor in Electrical Engineering. “It can also help enable practical applications that remain challenging, like automatic transcription of a...