  While neural network models are making significant progress in piano
transcription, they are becoming more resource-consuming due to requiring
larger model size and more computing power. In this paper, we attempt to apply
more prior about piano to reduce model size and improve the transcription
performance. The sound of a piano note contains various overtones, and the
pitch of a key does not change over time. To make full use of such latent
information, we propose HPPNet that using the Harmonic Dilated Convolution to
capture the harmonic structures and the Frequency Grouped Recurrent Neural
Network to model the pitch-invariance over time. Experimental results on the
MAESTRO dataset show that our piano transcription system achieves
state-of-the-art performance both in frame and note scores (frame F1 93.15%,
note F1 97.18%). Moreover, the model size is much smaller than the previous
state-of-the-art deep learning models.
