Sheet Music Statistical Layout Analysis

Campos, Vicente Bosch; Calvo-Zaragoza, Jorge; Toselli, Alejandro H.; Ruiz, Enrique Vidal

doi:10.1109/icfhr.2016.0066

Cited by 12 publications

(2 citation statements)

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“…To address full-page transcription-which suffers from having several music staves interacting together in a document-state-of-the-art OMR implements a two-stage pipeline. This workflow is composed of, first, a Layout Analysis step (Campos, Calvo-Zaragoza, Toselli, & Ruiz, 2016;Castellanos, Calvo-Zaragoza, & Iñesta, 2020;Pacha & Calvo-Zaragoza, 2018) that detects and extracts all the staves of the music score. This is typically addressed through object detection algorithms.…”

Section: Full-page Omr Transcriptionmentioning

confidence: 99%

On the Use of Transformers for End-to-End Optical Music Recognition

Ríos-Vila

Iñesta

Calvo-Zaragoza

2022

Lecture Notes in Computer Science

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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 (SMT), 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.

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Section: Full-page Omr Transcriptionmentioning

confidence: 99%

On the Use of Transformers for End-to-End Optical Music Recognition

Ríos-Vila

Iñesta

Calvo-Zaragoza

2022

Lecture Notes in Computer Science

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“…The received images are always individual staves, as explained previously, which are supposed to come from a previous document analysis process. Note that this is not a big assumption, as there are existing methods that effectively retrieve staff regions [9,29] The network we use for this work is, essentially, a recurrent neural network, as we are treating the input and the output as sequences. However, it is also appropriate to add a convolutional neural network, as we are also dealing with a computer vision problem.…”

Section: Neural Approachmentioning

confidence: 99%

Evaluating Simultaneous Recognition and Encoding for Optical Music Recognition

Ríos-Vila

Calvo-Zaragoza

Rizo

2020

7th International Conference on Digital Libraries for Musicology

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Most Optical Music Recognition workflows include several steps to retrieve the content from music score images. These steps typically comprise preprocessing, recognition, notation reconstruction and encoding. Currently, state-of-the-art models allow performing graphic recognition in an almost end-to-end fashion, performing the steps from preprocessing to recognition simultaneously. However, this graphic recognition has to be further processed to obtain a standard digital music representation. In this paper, we study the simultaneous recognition and encoding for a state-of-the-art OMR approach, based on neural networks, which receives a single staff-region image as input and directly obtains a sequence of characters that encodes the content in a standard music format. Our results confirm that performing OMR this way is feasible and brings additional benefits such as directly obtaining a version of the score readily available to be further processed or edited by standard tools. CCS CONCEPTS • Computing methodologies → Neural networks.

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