Abstract
Automatic music transcription (AMT) has achieved remarkable progress for instruments such as the piano, largely due to the availability of large-scale, high-quality datasets. In contrast, violin AMT remains underexplored due to limited annotated data. A common approach is to fine-tune pretrained models for other downstream tasks, but the effectiveness of such transfer remains unclear in the presence of timbral and articulatory differences. In this work, we investigate whether training from scratch on a medium-scale violin dataset can match the performance of fine-tuned piano-pretrained models. We adopt a piano transcription architecture without modification and train it on the MOSA dataset, which contains about 30 hours of aligned violin recordings. Our experiments on URMP and Bach10 show that models trained from scratch achieved competitive or even superior performance compared to fine-tuned counterparts. These findings suggest that strong violin AMT is possible without relying on pretrained piano representations, highlighting the importance of instrument-specific data collection and augmentation strategies.
Abstract (translated)
自动音乐转录（AMT）在钢琴等乐器上取得了显著进展，这主要得益于大规模、高质量数据集的可用性。相比之下，由于标注数据有限，小提琴 AMT 的研究相对较少。一种常见的方法是微调为其他下游任务预训练的模型，但在音色和发音差异存在的情况下，这种迁移的有效性仍不清楚。在这项工作中，我们调查了在中等规模的小提琴数据集上从头开始训练是否可以与钢琴预训练模型的微调性能相匹配。我们采用未经修改的钢琴转录架构，并将其在包含约30小时对齐小提琴录音的MOSA数据集上进行训练。我们在URMP和Bach10上的实验表明，从零开始训练的模型达到了与微调版本相当甚至更优的表现。这些发现表明，在不依赖于预训练钢琴表示的情况下，可以实现强大的小提琴AMT，这强调了乐器特定数据收集和增强策略的重要性。
URL
https://arxiv.org/abs/2508.13516
