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Analysis of institutional authors

Villarreal, MCorresponding AuthorSanchez, JaAuthor

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Proceedings Paper

Handwritten Music Recognition Improvement through Language Model Re-interpretation for Mensural Notation

Publicated to:Proceedings Of International Conference On Frontiers In Handwriting Recognition, Icfhr. 2020-September 199-204 - 2020-01-01 2020-September(), DOI: 10.1109/ICFHR2020.2020.00045

Authors: Villarreal, Manuel; Andreu Sanchez, Joan

Affiliations

Univ Politecn Valencia - Author

Abstract

Handwritten Music Recognition studies techniques for computers to transcribe handwritten musical notation that is registered in document images into electronic format, and to make this music available to the public. This task has been of great interest lately, as the technologies improve and can get better and better results on this problem. Recent machine intelligent approaches based on Deep and Recurrent Neural Networks have already shown how they work significantly better in the problem than traditional HMM-based approaches, especially when we are talking about Mensural Notation. These Neural Network-based researches have investigated the task of recognizing Mensural Notation as another written text recognition task, but have not explored the characteristics of musical elements in depth. Other papers have tried to dig deeper into analyzing musical elements and the extraction of their characteristics from segmented symbols, without reflecting this in holistic way. In this paper, we will try to make a complete recognition system directly from the scores, using techniques that enhance information obtained from symbols. We explore other language model interpretations and test our proposal on a publicly available dataset. In our experiments, we have made a 31% relative improvement in regards to error at the symbol level. With this, we have gone from a 3.91% absolute error rate, using Neural Network-based technology, to a 2.70% absolute error rate, by using language model re-interpretations.

Keywords

Absolute errorCharacter recognitionComputational linguisticsDeep neural networksDocument imagesElectronic formatsErrorsLanguage modelMusic recognitionMusical notationN/aRecognition systemsRecurrent neural networksStatistical testsWritten texts

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Proceedings Of International Conference On Frontiers In Handwriting Recognition, Icfhr due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Instrumentation.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 3.8, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-21, the following number of citations:

  • WoS: 6
  • Scopus: 7
  • OpenCitations: 5

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-21:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 5 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Villarreal Ruiz, Manuel) and Last Author (Sánchez Peiró, Joan Andreu).

the author responsible for correspondence tasks has been Villarreal Ruiz, Manuel.