  Piano cover generation aims to create a piano cover from a pop song. Existing
approaches mainly employ supervised learning and the training demands
strongly-aligned and paired song-to-piano data, which is built by remapping
piano notes to song audio. This would, however, result in the loss of piano
information and accordingly cause inconsistencies between the original and
remapped piano versions. To overcome this limitation, we propose a transfer
learning approach that pre-trains our model on piano-only data and fine-tunes
it on weakly-aligned paired data constructed without note remapping. During
pre-training, to guide the model to learn piano composition concepts instead of
merely transcribing audio, we use an existing lead sheet transcription model as
the encoder to extract high-level features from the piano recordings. The
pre-trained model is then fine-tuned on the paired song-piano data to transfer
the learned composition knowledge to the pop song domain. Our evaluation shows
that this training strategy enables our model, named PiCoGen2, to attain
high-quality results, outperforming baselines on both objective and subjective
metrics across five pop genres.
