  Recent advances in polyphonic piano transcription have been made primarily by
a deliberate design of neural network architectures that detect different note
states such as onset or sustain and model the temporal evolution of the states.
The majority of them, however, use separate neural networks for each note
state, thereby optimizing multiple loss functions, and also they handle the
temporal evolution of note states by abstract connections between the
state-wise neural networks or using a post-processing module. In this paper, we
propose a unified neural network architecture where multiple note states are
predicted as a softmax output with a single loss function and the temporal
order is learned by an auto-regressive connection within the single neural
network. This compact model allows to increase note states without
architectural complexity. Using the MAESTRO dataset, we examine various
combinations of multiple note states including on, onset, sustain, re-onset,
offset, and off. We also show that the autoregressive module effectively learns
inter-state dependency of notes. Finally, we show that our proposed model
achieves performance comparable to state-of-the-arts with fewer parameters.
