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This is really interesting, but it confused me at first. Given the title and the problem posed in the article's intro, I figured this would be a dataset of she

This is really interesting, but it confused me at first. Given the title and the problem posed in the article's intro, I figured this would be a dataset of sheet music, ie. the notes and durations specified in some printed music. However, reading more it appears to be focused on recordings (ie. audio) and annotating those recordings with information about where each note starts and stops. So to me this seems more directly applicable to transcription (ie. taking audio and turning it into sheet music) or synthesis (taking sheet music and turning it into audio of a human-sounding performance) than it does to composition or finishing unfinished works by famous composers. The output of the compositional process is generally sheet music, not audio, so it seems to make more sense that problems around composition would be trained and learn in the sheet music domain. I'm not a machine learning researcher though! This is just my impression as a musician.

jthickstun 10y ago
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 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!

jthickstun 10y ago

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 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!