In this study, we present a novel automatic piano reduction method with semi-supervised machine learning. Piano reduction is an important music transformation process, which helps musicians and composers as a musical sketch for performances and analysis. The automation of such is a highly challenging research problem but could bring huge conveniences as manually doing a piano reduction takes a lot of time and effort. While supervised machine learning is often a useful tool for learning input-output mappings, it is difficult to obtain a large quantity of labelled data. We aim to solve this problem by utilizing semi-supervised learning, so that the abundant available data in classical music can be leveraged to perform the task with little or no labelling effort. In this regard, we formulate a two-step approach of music simplification followed by harmonization. We further propose and implement two possible solutions making use of an existing machine learning framework -- MidiBERT. We show that our solutions can output practical and realistic samples with an accurate reduction that needs only small adjustments in post-processing. Our study forms the groundwork for the use of semi-supervised learning in automatic piano reduction, where future researchers can take reference to produce more state-of-the-art results.

翻译：本研究提出了一种采用半监督机器学习的新型自动钢琴缩编方法。钢琴缩编是一种重要的音乐转换过程，可作为演奏和分析的音乐草图，为音乐家和作曲家提供帮助。该过程的自动化是一个极具挑战性的研究课题，但能带来巨大便利，因为手动进行钢琴缩编需要耗费大量时间和精力。虽然监督式机器学习通常是学习输入-输出映射的有效工具，但难以获取大量标注数据。我们旨在通过利用半监督学习来解决这一问题，从而能够运用古典音乐中丰富的可用数据，以极少或无需标注的方式完成该任务。为此，我们构建了一个包含音乐简化与和声化处理的两步式方法框架。进一步地，我们基于现有机器学习框架——MidiBERT，提出并实现了两种可行的解决方案。实验表明，我们的解决方案能够生成实用且真实的样本，其缩编结果精确度高，仅需在后处理阶段进行微调即可使用。本研究为半监督学习在自动钢琴缩编领域的应用奠定了基础，未来研究者可借鉴本工作以取得更先进的成果。