用音符去噪和钢琴卷预测提升BERT对乐谱的理解能力
Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Prediction
- 通过噪声污染音符并让模型修复，学习音乐结构知识
- 在12个下游任务中表现优于基线，尤其在和弦识别上提升显著
- 适合音乐生成与分析研究者使用，尤其关注符号化乐谱
我们提出一种类似BERT的预训练模型，用于符号化音乐理解，在多个下游任务中表现出色。为此，设计了两种新预训练目标：音符修正和钢琴卷预测。首先，采样部分音符并施加有限噪声，训练模型进行去噪；其次，从带噪音符中预测条目级和局部钢琴卷表示。这些目标促使模型更好学习音高间隔等音乐知识。评估方面，构建涵盖12个任务的基准，包括和弦估计到符号化流派分类。结果验证了所提预训练目标在下游任务中的有效性。
原文摘要 · Abstract (English)
We propose a pre-trained BERT-like model for symbolic music understanding that achieves competitive performance across a wide range of downstream tasks. To achieve this target, we design two novel pre-training objectives, namely token correction and pianoroll prediction. First, we sample a portion of note tokens and corrupt them with a limited amount of noise, and then train the model to denoise the corrupted tokens; second, we also train the model to predict bar-level and local pianoroll-derived representations from the corrupted note tokens. We argue that these objectives guide the model to better learn specific musical knowledge such as pitch intervals. For evaluation, we propose a benchmark that incorporates 12 downstream tasks ranging from chord estimation to symbolic genre classification. Results confirm the effectiveness of the proposed pre-training objectives on downstream tasks.
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