{"ID":2825569,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21324","arxiv_id":"2512.21324","title":"Towards Practical Automatic Piano Reduction using BERT with Semi-supervised Learning","abstract":"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.","short_abstract":"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 b...","url_abs":"https://arxiv.org/abs/2512.21324","url_pdf":"https://arxiv.org/pdf/2512.21324v2","authors":"[\"Wan Ki Wong\",\"Ka Ho To\",\"Chuck-jee Chau\",\"Lucas Wong\",\"Kevin Y. Yip\",\"Irwin King\"]","published":"2025-12-24T18:48:49Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.SC\"]","methods":"[]","has_code":false}