Proceedings of the 4th International Workshop on Reading Music Systems

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 4th International Workshop on Reading Music Systems, held online on Nov. 18th 2022.Comment: Proceedings edited by Jorge Calvo-Zaragoza, Alexander Pacha and Elona Shatr

Understanding Optical Music Recognition

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: Few introductory materials are available, and, furthermore, the field has struggled with defining itself and building a shared terminology. In this work, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords

Diseño y construcción de un chasis liviano para un prototipo eléctrico monoplaza

En el presente trabajo se planteó como objetivo diseñar y construir un chasis liviano para un vehículo monoplaza eléctrico mediante los softwares, diseño asistido por computadora (CAD)/ ingeniería asistida por computadora (CAE) para una adecuada optimización del peso. Para la construcción del chasis se desarrolló como primer paso el dimensionamiento que debe cumplir el piloto a través de un estudio estadístico para determinar la masa y altura del mismo. Una vez concluida esta fase, se procedió con la selección de forma, tamaño y espesor de perfil, tomando en cuenta la disponibilidad en el medio, las prestaciones mecánicas y la facilidad al momento de construir, obteniendo como la mejor opción un perfil cuadrado de 1.25 pulgadas x 1.1 mm de espesor. Posteriormente se procedió con la selección de materiales y para ello se realizó la comparación de los mismos en diferentes fases del proceso, mediante matrices de decisión y simulaciones mediante software CAE, obteniendo como el material más óptimo para la manufacturación del chasis al aluminio 6063 T5, siendo 65% más liviano que el acero estructural y además cuenta con características mecánicas que complacen las necesidades de construcción. Finalmente, se realizó el proceso de manufactura del chasis, siendo fundamental por las buenas prestaciones que ofreció al momento de realizar pruebas reales. Para la verificación de la resistencia y prestaciones mecánicas, se realizó varios ensayos que determinaron la fiabilidad del chasis, por lo que cuenta con un factor de seguridad por fatiga de 3,065, resistiendo satisfactoriamente a las cargas fluctuantes aplicadas. Se concluye que el chasis fue diseñado y construido mediante software CAD /CAE teniendo una masa de 10.5 Kg, además de presentar una alta resistencia. Se recomienda que para ciertas eventualidades o pandeo se podría aumentar el espesor del perfil o a su vez reforzar la estructura con fibra de carbono.This research aimed to design and build a lightweight chassis for an electric single-seater vehicle through software, Computer-aided design (CAD), and Computer-aided engineering (CAE) for adequate weight optimization. The dimensioning that the pilot must comply with was developed as a first step for the construction of the chassis, through a statistical study to determine its mass and height. Once this phase was completed, the shape, size and thickness of the profile were selected, taking into account available in the environment, mechanical performance, and constructability, obtaining a square profile of 1.25 inches x 1.1 mm thick was the best option. Subsequently, we proceeded with the selection of materials and for this, they were compared in different phases of the process. Through decision matrices and simulations using CAE software, obtaining aluminum 6063 TS as the most optimal material for the manufacture of the chassis. Being 65% lighter than structural steel and also has mechanical characteristics that meet construction needs. Finally, the chassis manufacturing process was carried out, which is essential for the good performance offered at the time of real tests. To verify the resistance and mechanical performance, several tests were carried out to determine the reliability of the chassis, which is why it has a fatigue safety factor of 3.065, satisfactorily resisting the fluctuating loads applied. It is concluded that the chassis was designed and built using CAD / CAE software having a mass of 10.5 Kg, in addition to presenting a high resistance. It is recommended that for certain eventualities or buckling, the thickness of the profile could be increased or, in turn, the structure reinforced with carbon fiber

Building a Comprehensive Sheet Music Library Application

Digital symbolic music scores offer many benefits compared to paper-based scores, such as a flexible dynamic layout that allows adjustments of size and style, intelligent navigation features, automatic page-turning, on-the-fly modifications of the score including transposition into a different key, and rule-based annotations that can save hours of manual work by automatically highlighting relevant aspects in the score. However, most musicians still rely on paper because they don’t have access to a digital version of their sheet music, or their digital solution does not provide a satisfying experience. To bring digital scores to millions of musicians, we at Enote are building a mobile application that offers a comprehensive digital library of sheet music. These scores are obtained by a large-scale Optical Music Recognition process, combined with metadata collection and curation. Our material is stored in the MEI format and we rely on Verovio as a central component of our app to present scores and parts dynamically on mobile devices. This combination of the expressiveness of MEI with the beautiful engraving of Verovio allows us to create a flexible, mobile solution that we believe to be a powerful and true alternative to paper scores with practical features like smart annotations or instant transpositions. We also invest heavily into the open-source development of Verovio to make it the gold standard for rendering beautiful digital sheet music

The DeepScoresV2 dataset and benchmark for music object detection

The dataset, code and pre-trained models, as well as user instructions, are publicly available at https://zenodo.org/record/4012193.In this paper, we present DeepScoresV2, an extended version of the DeepScores dataset for optical music recognition (OMR). We improve upon the original DeepScores dataset by providing much more detailed annotations, namely (a) annotations for 135 classes including fundamental symbols of non-fixed size and shape, increasing the number of annotated symbols by 23%; (b) oriented bounding boxes; (c) higher-level rhythm and pitch information (onset beat for all symbols and line position for noteheads); and (d) a compatibility mode for easy use in conjunction with the MUSCIMA++ dataset for OMR on handwritten documents. These additions open up the potential for future advancement in OMR research. Additionally, we release two state-of-the-art baselines for DeepScoresV2 based on Faster R-CNN and the Deep Watershed Detector. An analysis of the baselines shows that regular orthogonal bounding boxes are unsuitable for objects which are long, small, and potentially rotated, such as ties and beams, which demonstrates the need for detection algorithms that naturally incorporate object angles

Selbstlernende Optische Notenerkennung

Zusammenfassung in deutscher SpracheMusik ist ein essenzieller Teil unserer Kultur und unseres Erbes. Durch die Jahrhunderte wurden Millionen an Liedern komponiert und mittels Musiknotation auf Papier festgehalten. Die optische Notenerkennung (engl. Optical Music Recognition, kurz OMR) ist das Forschungsfeld, das untersucht, wie der Computer das Lesen von Musiknoten erlernen kann. Trotz jahrzehntelanger Forschung, gilt die optische Notenerkennung bis heute als alles andere als gelöst. Ein Grund hierfür ist die Tatsache, dass viele traditionelle Ansätze auf Heuristiken beruhen, die sich nur schwer verallgemeinern lassen. Deshalb schlage ich in dieser Arbeit einen anderen Weg vor, nämlich den Computer das Lesen von Musiknoten selbstständig erlernen zu lassen, mittels maschinellem Lernen, insbesondere Deep Learning. In zahlreichen Experimenten konnte ich demonstrieren, dass der Computer unter Überwachung des Lernprozesses die meisten Herausforderungen der optischen Notenerkennung robust erlernen kann. Zu diesen Herausforderungen zählen die Analyse der Dokumentenstruktur, die Erkennung und Klassifikation von Symbolen, sowie die Konstruktion von einem Musiknotationsgraphen, der als zwischenzeitliche Repräsentation fungiert, die in ein passendes Format zur Weiterverarbeitung exportiert werden kann. Ein trainiertes neuronales Netzwerk kann zuverlässig vorhersagen, ob ein Bild Noten enthält oder nicht, während ein anderes imstande ist, den selben Takt in verschiedenen Ausgaben derselben Musik zu finden und miteinander zu verknüpfen, sodass man bequem zwischen diesen hin und her navigieren kann. Die Erkennung von Symbolen in gesetzten und handgeschriebenen Noten kann ebenfalls erlernt werden, sofern man ausreichend annotierte Daten zur Verfügung hat. Die Klassifikation der erkannten Symbole hat sogar eine niedrigere Fehlerrate als die von Menschen. Für Noten, die in Mensurnotation verfasst wurden, kann man die gesamte Erkennung in drei Schritte vereinfachen, wovon zwei mittels maschinellem Lernen gelöst werden können. Neben dem Verfassen von wissenschaftlichen Artikeln, habe ich auch die größte Sammlung von Datensätzen für OMR zusammengetragen und dokumentiert, sowie die wahrscheinlich umfangreichste Bibliographie, die derzeit verfügbar ist. Beide Sammlungen sind online verfügbar. Desweiteren war ich an der Organisation des 1st International Workshop on Reading Music Systems beteiligt, habe gemeinsam mit Kollegen ein Tutorial bei der International Society For Music Information Retrieval Conference zum Thema optischer Notenerkennung gegeben, und ein weiterer Workshop bei der Music Encoding Conference findet im Sommer 2019 statt. Viele Herausforderungen der optischen Notenerkennung können mit Deep Learning effizient gelöst werden, wie die Analyse des Layouts oder die Erkennung von Musikobjekten. Allerdings ist die Musiknotation ein strukturelles Schreibsystem, bei dem die Beziehungen und das Zusammenspiel zwischen den einzelnen Objekten die Semantik bestimmen. Ein Musiknotationgraph ist eine geeignete Datenstruktur um diese Information abzubilden und erlaubt es klar zwischen zwei Dingen zu unterscheiden: der Rekonstruktion von Informationen aus dem Bild und der Kodierung der rekonstruierten Information in ein bestimmtes Format unter Berücksichtigung der Regeln der Musiknotation. So eine Konstruktion eines Musiknotationsgraphen kann zwar erlernt werden, bleiben einige Forschungsfragen offen. Ich bin zuversichtlich, dass das Trainieren des Computers auf einem hinreichend großen Datensatz unter menschlicher Überwachung einen nachhaltigen Ansatz darstellt, mit dem man in Zukunft viele Anwendungsfälle der optischen Notenerkennung lösen wird können.Music is an essential part of our culture and heritage. Throughout the centuries, millions of songs were composed and written down in documents using music notation. Optical Music Recognition (OMR) is the research field that investigates how the computer can learn to read those documents. Despite decades of research, OMR is still considered far from being solved. One reason is that traditional approaches rely heavily on heuristics and often do not generalize well. In this thesis, I propose a different approach to let the computer learn to read music notation documents mostly by itself using machine learning, especially deep learning. In several experiments, I have demonstrated that the computer can learn to robustly solve many tasks involved in OMR by using supervised learning. These include the structural analysis of the document, the detection and classification of symbols in the scores as well as the construction of the music notation graph, which is an intermediate representation that can be exported into a format suitable for further processing. A trained deep convolutional neural network can reliably detect whether an image contains music or not, while another one is capable of finding and linking individual measures across multiple sources for easy navigation between them. Detecting symbols in typeset and handwritten scores can be learned, given a sufficient amount of annotated data, and classifying isolated symbols can be performed at even lower error rates than those of humans. For scores written in mensural notation the complete recognition can even be simplified into just three steps, two of which can be solved with machine learning. Apart from publishing a number of scientific articles, I have gathered and documented the most extensive collection of datasets for OMR as well as the probably most comprehensive bibliography currently available. Both are available online. Moreover I was involved in the organization of the International Workshop on Reading Music Systems, in a joint tutorial at the International Society For Music Information Retrieval Conference on OMR as well as in another workshop at the Music Encoding Conference. Many challenges of OMR can be solved efficiently with deep learning, such as the layout analysis or music object detection. As music notation is a configurational writing system where the relations and interplay between symbols determine the musical semantic, these relationships have to be recognized as well. A music notation graph is a suitable representation for storing this information. It allows to clearly distinguish between the challenges involved in recovering information from the music score image and the encoding of the recovered information into a specific output format while complying with the rules of music notation. While the construction of such a graph can be learned as well, there are still many open issues that need future research. But I am confident that training the computer on a sufficiently large dataset under human supervision is a sustainable approach that will help to solve many applications of OMR in the future.14

Building a Comprehensive Sheet Music Library Application

Digital symbolic music scores offer many benefits compared to paper-based scores, such as a flexible dynamic layout that allows adjustments of size and style, intelligent navigation features, automatic page-turning, on-the-fly modifications of the score including transposition into a different key, and rule-based annotations that can save hours of manual work by automatically highlighting relevant aspects in the score. However, most musicians still rely on paper because they don’t have access to a digital version of their sheet music, or their digital solution does not provide a satisfying experience. To bring digital scores to millions of musicians, we at Enote are building a mobile application that offers a comprehensive digital library of sheet music. These scores are obtained by a large-scale Optical Music Recognition process, combined with metadata collection and curation. Our material is stored in the MEI format and we rely on Verovio as a central component of our app to present scores and parts dynamically on mobile devices. This combination of the expressiveness of MEI with the beautiful engraving of Verovio allows us to create a flexible, mobile solution that we believe to be a powerful and true alternative to paper scores with practical features like smart annotations or instant transpositions. We also invest heavily into the open-source development of Verovio to make it the gold standard for rendering beautiful digital sheet music

COVID-19 un hito para la economía, una mirada analítica a la realidad Económica del Ecuador

Se desarrolló esta investigación con el objetivo de analizar el impacto de la Pandemia Covid-19 en la economía mundial y sus efectos en el Ecuador. La metodología empleada fue la cualitativa y consistió en le revisión de fuentes bibliográficas como artículos científicos, tesis e información verificada en fuentes oficiales como el Fondo Monetario Internacional, Organización Mundial del Comercio, Comisión Económica para Latinoamérica y el Caribe e instituciones nacionales como el Ministerio de Salud Pública, Ministerios de Economía y Finanzas y Ministerio de Industria y Producción. La investigación arrojó que el decrecimiento del Producto Interno Bruto a nivel mundial ha sufrido una baja considerable ya que restricciones aplicadas en cada país ha conllevado el cierre de fronteras y por tanto una limitación a la importación y exportación, el panorama sigue siendo sombrío más aún en países con mayor vulnerabilidad económica, por lo tanto la salvación económica dependerá de un buen uso de los recursos públicos, austeridad y una política internacional que ayude a la liquidez

A Baseline for General Music Object Detection with Deep Learning

Deep learning is bringing breakthroughs to many computer vision subfields including Optical Music Recognition (OMR), which has seen a series of improvements to musical symbol detection achieved by using generic deep learning models. However, so far, each such proposal has been based on a specific dataset and different evaluation criteria, which made it difficult to quantify the new deep learning-based state-of-the-art and assess the relative merits of these detection models on music scores. In this paper, a baseline for general detection of musical symbols with deep learning is presented. We consider three datasets of heterogeneous typology but with the same annotation format, three neural models of different nature, and establish their performance in terms of a common evaluation standard. The experimental results confirm that the direct music object detection with deep learning is indeed promising, but at the same time illustrates some of the domain-specific shortcomings of the general detectors. A qualitative comparison then suggests avenues for OMR improvement, based both on properties of the detection model and how the datasets are defined. To the best of our knowledge, this is the first time that competing music object detection systems from the machine learning paradigm are directly compared to each other. We hope that this work will serve as a reference to measure the progress of future developments of OMR in music object detection

DeepScoresV2

The DeepScoresV2 Dataset for Music Object Detection contains digitally rendered images of written sheet music, together with the corresponding ground truth to fit various types of machine learning models. A total of 151 Million different instances of music symbols, belonging to 135 different classes are annotated. The total Dataset contains 255,385 Images. For most researches, the dense version, containing 1714 of the most diverse and interesting images, is a good starting point. The dataset contains ground in the form of: - Non-oriented bounding boxes - Oriented bounding boxes - Semantic segmentation - Instance segmentatio