近日，多媒体检索领域国际会议ACM International Conference on Multimedia Retrieval (ICMR 2023)在希腊塞萨洛尼基举办，硕士生沈哲旭关于符号音乐理解的研究成果被录用为短文，并在会议上做海报展示和口头汇报。ICMR是多媒体检索领域的重要国际会议，自2011年开始每年举办，被CCF推荐为B类会议。此次会议吸引了来自世界各地的研究人员和从业人员，展示了多媒体检索、推荐及相关领域的重要和创新性研究。

题目：More Than Simply Masking: Exploring Pre-training Strategies for Symbolic Music Understanding

摘要：Pre-trained language models have become the prevailing approach for handling natural language processing tasks in recent years. Given the similarities in sequential features between symbolic music and natural language text, it is fairly logical to adopt pre-training methods to symbolic music data. However, the disparity between music and natural language text makes it difficult to comprehensively model the unique features of music through traditional text-based pre-training strategies alone. To address this challenge, in this paper, we design the Quad-attribute Masking (QM) strategy and propose the Key Prediction (KP) task to improve the extraction of generic knowledge from symbolic music. We evaluate the impact of various pre-training strategies on several public symbolic music datasets, and the results of our experiments reveal that the proposed multi-task pre-training model can effectively capture music domain knowledge from symbolic music data and significantly improve performance on downstream tasks.

近年来，预训练的语言模型已经成为处理自然语言处理任务的主流方法。鉴于符号音乐和自然语言文本之间在顺序特征上的相似性，对符号音乐数据采用预训练方法是较为合理的。然而，由于音乐和自然语言文本之间的差异，仅仅通过传统的基于文本的预训练策略，很难全面地建模音乐的独有特征。为了应对这一挑战，在本文中，我们设计了四属性屏蔽（QM）策略，并提出了调式预测（KP）任务，以改善对符号音乐通用知识的提取。我们评估了各种预训练策略在几个公开符号音乐数据集上的表现，实验结果显示，所提出的多任务预训练模型能够有效地从符号音乐数据中捕获音乐领域的知识，并显著提高下游任务的性能。