Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription

TL;DR

The paper introduces the Sheet Music Transformer, an end-to-end model that transcribes complex polyphonic scores directly from images, surpassing existing methods in accuracy and scalability.

Contribution

It presents the first Transformer-based end-to-end OMR model capable of handling polyphonic music scores without monophonic simplifications.

Findings

Outperforms state-of-the-art OMR methods on two datasets.

Effectively transcribes complex polyphonic scores.

Demonstrates scalability to intricate musical structures.

Abstract

State-of-the-art end-to-end Optical Music Recognition (OMR) has, to date, primarily been carried out using monophonic transcription techniques to handle complex score layouts, such as polyphony, often by resorting to simplifications or specific adaptations. Despite their efficacy, these approaches imply challenges related to scalability and limitations. This paper presents the Sheet Music Transformer, the first end-to-end OMR model designed to transcribe complex musical scores without relying solely on monophonic strategies. Our model employs a Transformer-based image-to-sequence framework that predicts score transcriptions in a standard digital music encoding format from input images. Our model has been tested on two polyphonic music datasets and has proven capable of handling these intricate music structures effectively. The experimental outcomes not only indicate the competence of the model, but also show that it is better than the state-of-the-art methods, thus contributing to advancements in end-to-end OMR transcription.