A diff procedure for music score files

Foscarin, Francesco; Jacquemard, Florent; Fournier-S’niehotta, Raphaël

doi:10.1145/3358664.3358671

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…Two visualization errors from both the Camera GrandStaff and Quartets dataset are shown in Figures 5 and 7, respectively. The highlighted errors were computed using the musicdiff tool [16] and visualized with the MuseScore editor 13 . Both examples show that, visually, the model is capable of generating a correct music sequence in terms of syntax, as no bar-completion or time-related errors are found.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…It consists of a tree-based decomposition of a music score that reflects its temporal organization. This draws heavily from [25][26][27], which introduced into the music information retrieval literature some ideas and tools from the fields of databases systems and computer linguistics (e.g., hierarchical decomposition of musical content and context-free grammars). Recently, the authors of [28] used a similar graph-based representation to study music similarity.…”

Section: Related Workmentioning

confidence: 99%

“…In all cases, the rhythmic decomposition rules can be expressed in a well-known formal language, namely Context-Free Grammars (CFG). In order to express decomposition preferences, they can be extended to Weighted Context-Free Grammars [26]. As an illustration, the following grammar G = (V, Mus, R, S) is sufficient to model the rhythmic organization of our example, with time signature 4/4.…”

Section: Music Content Descriptorsmentioning

confidence: 99%

A Framework for Content-Based Search in Large Music Collections

Zhu

Fournier-S’niehotta

Rigaux

et al. 2022

BDCC

Self Cite

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We address the problem of scalable content-based search in large collections of music documents. Music content is highly complex and versatile and presents multiple facets that can be considered independently or in combination. Moreover, music documents can be digitally encoded in many ways. We propose a general framework for building a scalable search engine, based on (i) a music description language that represents music content independently from a specific encoding, (ii) an extendible list of feature-extraction functions, and (iii) indexing, searching, and ranking procedures designed to be integrated into the standard architecture of a text-oriented search engine. As a proof of concept, we also detail an actual implementation of the framework for searching in large collections of XML-encoded music scores, based on the popular ElasticSearch system. It is released as open-source in GitHub, and available as a ready-to-use Docker image for communities that manage large collections of digitized music documents.

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“…The most important drawback of this approach is that it depends on other aspects such as the usability of the notation editor utilized. Some authors calculate the edit distance between the expected encoding and the actual one [12]. However, if the encoding is based on an XML format, the actual effort to correct the output is not directly proportional to the edit distance obtained.…”

Section: Evaluation and Practical Applicationmentioning

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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A diff procedure for music score files

Cited by 8 publications

References 15 publications

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

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

A Framework for Content-Based Search in Large Music Collections

Evaluating Simultaneous Recognition and Encoding for Optical Music Recognition

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