Instrument playing techniques (IPTs) constitute a pivotal component of
musical expression. However, the development of automatic IPT detection methods
suffers from limited labeled data and inherent class imbalance issues. In this
paper, we propose to apply a self-supervised learning model pre-trained on
large-scale unlabeled music data and finetune it on IPT detection tasks. This
approach addresses data scarcity and class imbalance challenges. Recognizing
the significance of pitch in capturing the nuances of IPTs and the importance
of onset in locating IPT events, we investigate multi-task finetuning with
pitch and onset detection as auxiliary tasks. Additionally, we apply a
post-processing approach for event-level prediction, where an IPT activation
initiates an event only if the onset output confirms an onset in that frame.
Our method outperforms prior approaches in both frame-level and event-level
metrics across multiple IPT benchmark datasets. Further experiments demonstrate
the efficacy of multi-task finetuning on each IPT class.

使用自监督学习模型对大规模未标记音乐数据进行预训练并在乐器演奏技术检测任务中进行微调，以解决数据稀缺和类别不平衡问题，通过辅助任务的多任务微调和事件级别预测的后处理方法，该方法在多个乐器演奏技术基准数据集上在帧级别和事件级别指标上均优于先前方法。

MERTech：利用自监督预训练模型和多任务微调进行乐器演奏技术检测

MERTech: Instrument Playing Technique Detection Using Self-Supervised  Pretrained Model With Multi-Task Finetuning

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。