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.

通过修正加性注意机制，本文全面检验了 Onsets-and-Frames AMT 模型并找出了对 AMT 性能有贡献的关键组件，实验结果表明加性注意机制在适度的时间上下文后并不对模型有益处，规则 - based 后处理高度贡献 SOTA 表现，并且无论模型的复杂程度如何，onsets 始终是最重要的注意机制特征。这些发现鼓励 AMT 的研究更注重强大的开始检测器和有效的后处理。