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Generating musical audio directly with neural networks is notoriouslydifficult because it requires coherently modeling structure at many differenttimescales. Fortunately, most music is also highly structured and can berepresented as discrete note events played on musical instruments. Herein, weshow that by using notes as an intermediate representation, we can train asuite of models capable of transcribing, composing, and synthesizing audiowaveforms with coherent musical structure on timescales spanning six orders ofmagnitude (~0.1 ms to ~100 s), a process we call Wave2Midi2Wave. This largeadvance in the state of the art is enabled by our release of the new MAESTRO(MIDI and Audio Edited for Synchronous TRacks and Organization) dataset,composed of over 172 hours of virtuosic piano performances captured with finealignment (~3 ms) between note labels and audio waveforms. The networks and thedataset together present a promising approach toward creating new expressiveand interpretable neural models of music.

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset