The neural semi-Markov Conditional Random Field (semi-CRF) framework has demonstrated promise for event-based piano transcription. In this framework, all events (notes or pedals) are represented as closed intervals tied to specific event types. The neural semi-CRF approach requires an interval scoring matrix that assigns a score for every candidate interval. However, designing an efficient and expressive architecture for scoring intervals is not trivial. In this paper, we introduce a simple method for scoring intervals using scaled inner product operations that resemble how attention scoring is done in transformers. We show theoretically that, due to the special structure from encoding the non-overlapping intervals, under a mild condition, the inner product operations are expressive enough to represent an ideal scoring matrix that can yield the correct transcription result. We then demonstrate that an encoder-only non-hierarchical transformer backbone, operating only on a low-time-resolution feature map, is capable of transcribing piano notes and pedals with high accuracy and time precision. The experiment shows that our approach achieves the new state-of-the-art performance across all subtasks in terms of the F1 measure on the Maestro dataset.

翻译：神经半马尔可夫条件随机场（semi-CRF）框架在基于事件的钢琴转录任务中展现了良好的前景。在该框架中，所有事件（音符或踏板）均表示为与特定事件类型相关联的闭合区间。神经半马尔可夫条件随机场方法需要一个区间评分矩阵，为每个候选区间分配一个评分。然而，设计高效且富有表达力的区间评分架构并非易事。本文提出一种基于缩放内积运算的简单区间评分方法，其原理类似于Transformer中的注意力评分机制。我们从理论上证明，由于非重叠区间编码的特殊结构，在温和条件下，内积运算足以表达能够生成正确转录结果的理想评分矩阵。进一步实验表明，仅处理低时间分辨率特征图的纯编码器非层级Transformer主干网络，能够高精度且高时间精度地转录钢琴音符与踏板。在Maestro数据集上的实验显示，我们的方法在所有子任务的F1指标上均达到了新的最优性能水平。