Towards Practical Automatic Piano Reduction using BERT with Semi-supervised Learning

TL;DR

This paper introduces a semi-supervised machine learning approach using MidiBERT for automatic piano reduction, reducing the need for labeled data and producing realistic musical outputs.

Contribution

It presents a novel semi-supervised learning framework for piano reduction leveraging MidiBERT, enabling effective automation with minimal labeled data.

Findings

Outputs practical and realistic piano reductions

Requires only small post-processing adjustments

Lays groundwork for future semi-supervised music reduction methods

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.