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Piano Genie

Chris Donahue
Sander Dieleman
ACM IUI (2019)
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Abstract

We present Piano Genie, a generative musical instrument which allows non-musicians to play the piano. With Piano Genie, a user performs on a simple interface with eight buttons, and their performance is decoded into the space of plausible piano music in real time. To learn a suitable mapping procedure for this problem, we train recurrent neural network autoencoders with discrete bottlenecks: an encoder learns an appropriate sequence of buttons corresponding to a piano piece, and a decoder learns to map this sequence back to the original piece. During performance, we substitute a user’s input for the encoder output, and play the decoder’s prediction each time the user presses a button. To improve the interpretability of Piano Genie’s performance mechanics, we impose musically-informed constraints over the encoder’s outputs.

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