Automatic Music Transcription (AMT), aiming to get musical notes from raw audio, typically uses frame-level systems with piano-roll outputs or language model (LM)-based systems with note-level predictions. However, frame-level systems require manual thresholding, while the LM-based systems struggle with long sequences. In this paper, we propose a hybrid method combining pre-trained roll-based encoders with an LM decoder to leverage the strengths of both methods. Besides, our approach employs a hierarchical prediction strategy, first predicting onset and pitch, then velocity, and finally offset. The hierarchical prediction strategy reduces computational costs by breaking down long sequences into different hierarchies. Evaluated on two benchmark roll-based encoders, our method outperforms traditional piano-roll outputs 0.01 and 0.022 in onset-offset-velocity F1 score, demonstrating its potential as a performance-enhancing plug-in for arbitrary roll-based music transcription encoder.

本研究解决了自动音乐转录中的长序列处理和手动阈值问题，提出了一种结合预训练滚动编码器和语言模型解码器的混合方法。通过分层预测策略，让我们在 onset、音高、音量和 offset 的预测中降低计算成本，评估结果显示该方法在性能上明显优于传统的钢琴滚动输出。

基于预训练滚动编码器的分层语言建模钢琴转录