[jthickstun]

Hi haberman,

I'm one of the authors on this paper. You're right that the most direct applications of this dataset are transcription and synthesis. One of the cool aspects of end-to-end learning models is that they discover a "representation" of data that can be useful when applied to other tasks. We speculate about some tasks like recommendation and composition on our website:

http://homes.cs.washington.edu/~thickstn/musicnet.html

We're also interested in music like jazz and pop, for which good scores are often unavailable. Classical music is nice for training models because we can use sheet music as labels to learn a representation. Many aspects of this representation, such as rhythm and harmony, may transfer to other musical genres. Learning about classical recordings could bootstrap learning for other kinds of musical audio.

So while you're right that it's probably easier to learn a model to complete Bach using symbolic sheet music, we feel that addressing complex tasks directly from raw audio is worthwhile!