MusicScore: A Dataset for Music Score Modeling and Generation

TL;DR

MusicScore is a large-scale dataset of music score images with rich metadata, designed to facilitate research in music score modeling and generation, and includes a diffusion-based score generation system as a benchmark.

Contribution

The paper introduces MusicScore, a comprehensive large-scale dataset for music score modeling and generation, filling a gap in existing resources and enabling new research directions.

Findings

MusicScore includes 400, 14k, and 200k image-text pairs at different scales.

A diffusion-based score generation system is developed and benchmarked using the dataset.

MusicScore is publicly available for research use.

Abstract

Music scores are written representations of music and contain rich information about musical components. The visual information on music scores includes notes, rests, staff lines, clefs, dynamics, and articulations. This visual information in music scores contains more semantic information than audio and symbolic representations of music. Previous music score datasets have limited sizes and are mainly designed for optical music recognition (OMR). There is a lack of research on creating a large-scale benchmark dataset for music modeling and generation. In this work, we propose MusicScore, a large-scale music score dataset collected and processed from the International Music Score Library Project (IMSLP). MusicScore consists of image-text pairs, where the image is a page of a music score and the text is the metadata of the music. The metadata of MusicScore is extracted from the general information section of the IMSLP pages. The metadata includes rich information about the composer, instrument, piece style, and genre of the music pieces. MusicScore is curated into small, medium, and large scales of 400, 14k, and 200k image-text pairs with varying diversity, respectively. We build a score generation system based on a UNet diffusion model to generate visually readable music scores conditioned on text descriptions to benchmark the MusicScore dataset for music score generation. MusicScore is released to the public at https://huggingface.co/datasets/ZheqiDAI/MusicScore.