  Viewing polyphonic piano transcription as a multitask learning problem, where
we need to simultaneously predict onsets, intermediate frames and offsets of
notes, we investigate the performance impact of additional prediction targets,
using a variety of suitable convolutional neural network architectures. We
quantify performance differences of additional objectives on the large MAESTRO
dataset.
