  Piano covers of pop music are enjoyed by many people. However, the task of
automatically generating piano covers of pop music is still understudied. This
is partly due to the lack of synchronized {Pop, Piano Cover} data pairs, which
made it challenging to apply the latest data-intensive deep learning-based
methods. To leverage the power of the data-driven approach, we make a large
amount of paired and synchronized {Pop, Piano Cover} data using an automated
pipeline. In this paper, we present Pop2Piano, a Transformer network that
generates piano covers given waveforms of pop music. To the best of our
knowledge, this is the first model to generate a piano cover directly from pop
audio without using melody and chord extraction modules. We show that
Pop2Piano, trained with our dataset, is capable of producing plausible piano
covers.
