  Recent advances in automatic music transcription (AMT) have achieved highly
accurate polyphonic piano transcription results by incorporating onset and
offset detection. The existing literature, however, focuses mainly on the
leverage of deep and complex models to achieve state-of-the-art (SOTA)
accuracy, without understanding model behaviour. In this paper, we conduct a
comprehensive examination of the Onsets-and-Frames AMT model, and pinpoint the
essential components contributing to a strong AMT performance. This is achieved
through exploitation of a modified additive attention mechanism. The
experimental results suggest that the attention mechanism beyond a moderate
temporal context does not benefit the model, and that rule-based
post-processing is largely responsible for the SOTA performance. We also
demonstrate that the onsets are the most significant attentive feature
regardless of model complexity. The findings encourage AMT research to weigh
more on both a robust onset detector and an effective post-processor.
