  Most of the state-of-the-art automatic music transcription (AMT) models break
down the main transcription task into sub-tasks such as onset prediction and
offset prediction and train them with onset and offset labels. These
predictions are then concatenated together and used as the input to train
another model with the pitch labels to obtain the final transcription. We
attempt to use only the pitch labels (together with spectrogram reconstruction
loss) and explore how far this model can go without introducing supervised
sub-tasks. In this paper, we do not aim at achieving state-of-the-art
transcription accuracy, instead, we explore the effect that spectrogram
reconstruction has on our AMT model. Our proposed model consists of two U-nets:
the first U-net transcribes the spectrogram into a posteriorgram, and a second
U-net transforms the posteriorgram back into a spectrogram. A reconstruction
loss is applied between the original spectrogram and the reconstructed
spectrogram to constrain the second U-net to focus only on reconstruction. We
train our model on three different datasets: MAPS, MAESTRO, and MusicNet. Our
experiments show that adding the reconstruction loss can generally improve the
note-level transcription accuracy when compared to the same model without the
reconstruction part. Moreover, it can also boost the frame-level precision to
be higher than the state-of-the-art models. The feature maps learned by our
U-net contain gridlike structures (not present in the baseline model) which
implies that with the presence of the reconstruction loss, the model is
probably trying to count along both the time and frequency axis, resulting in a
higher note-level transcription accuracy.
