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

Staff-line removal with selectional auto-encoders

Staff-line removal is an important preprocessing stage as regards most Optical Music Recognition systems. The common procedures employed to carry out this task involve image processing techniques. In contrast to these traditional methods, which are based on hand-engineered transformations, the problem can also be approached from a machine learning point of view if representative examples of the task are provided. We propose doing this through the use of a new approach involving auto-encoders, which select the appropriate features of an input feature set (Selectional Auto-Encoders). Within the context of the problem at hand, the model is trained to select those pixels of a given image that belong to a musical symbol, thus removing the lines of the staves. Our results show that the proposed technique is quite competitive and significantly outperforms the other state-of-art strategies considered, particularly when dealing with grayscale input images.This work was partially supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU fellowship (AP2012- 0939) and the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R, supported by UE FEDER funds)

Symbol Recognition: Current Advances and Perspectives

Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content

Information technological aspects in the field of music. Overview

Uurimuse põhieesmärgiks on anda lugejale ülevaade nootide (noodilehtede) ettevalmistamist ja muusika esitamist toetavatest tarkvarapakettidest ning tutvustada olulisemaid aspekte, mis on seotud nende rakendamisega muusikavallas. Üksikasjaliku ülevaate esitab töö tulemusena valminud veebisõelmete andmebaas koos seda esitava veebirakendusega, mis sisaldab nimetatud tarkvarapakette iseloomustavaid kirjeid. Töö tekstiline osa, st dokument, kirjeldab kokkuvõtlikult olulisemaid aspekte koos mõningate tarkvaraliste näidetega. Osutub, et kõige rohkem leidub internetis noodigraafika töötlemise ning diginoodiks teisendamise vahendeid – vastavalt 98 ja 13 rakendust. Nende valdkondadega seotud töö jaotistes sätestatakse erinevad kriteeriumid, mida nimetatud rakenduste andmebaasi kandmisel arvesse võeti, aga ka meetodeid ja probleeme, millega vastavate rakenduste kasutamisel arvestada tuleks. Uurimust alustades oli üks esmaseid eesmärke koguda võimalikult palju informatsiooni intelligentsete muusikaseadmete, eelkõige elektroonilis-intelligentsete noodipultide kohta. Paraku leidub just nimelt selles valdkonnas kõige vähem vahendeid – kokku vaid 4 rakendust, millest reaalselt kasutatav on vaid üks. Töös kirjeldatakse rakenduste võimalikke omavahelisi võrdlusmomente, analüüsitakse vaadeldava valdkonna nüansse ning tutvustatakse arenguperspektiive. Informatiivsuse huvides on esitletud aga ka tarkvarakomponente ja -pakette (sh raamistikke), mis kaudselt toetavad nootide (noodilehtede) ettevalmistamist ning muusika esitamist – kokku 55 kirjet. Lisaks kirjeldatakse muusikaõpet toetavaid vahendeid. Nendest on andmebaasi kantud kokku 14 rakendust. Antakse põgus ülevaade olemasolevatest huvitavamatest noodikogudest ning nende kasutamisvõimalustest; andmebaasi lisatud vastavalt 13 kirjet. Tutvustatakse aga ka uurimuse kontekstiga seotud bibliograafiat ning ühte tuntumat konverentsiseeriat (ISMIR), mille raames on paljud publikatsioonid valminud. Publikatsioonide loetelu on samuti lisatud töö käigus valminud andmebaasi – kokku 113 kirjet. Arvestades, et pakettide kasutajaliidesed on reeglina ingliskeelsed, on koostatud vastav inglise-eesti terminisõnastik.The main purpose of this thesis is to give an overview of the existing software packages and tools, oriented towards the simplification of musicians everyday work. Since the field is quite extensive, only a subset of the available software has been taken into account – mainly programs designed to support preparing and interpreting sheet music. The thesis is divided into two major components – a database (appended on a CD), which contains all the information about the collected data (software, hardware, related bibliography, etc) and the document itself, where the criterions for comparing the software packages are listed and explained together with some illustrative examples. The first two chapters of the document are dedicated to the ways of generating sheet music – describing and comparing the different software tools for displaying and editing sheet music using note graphics software. Also, an overview of intelligent music stands, which is still an underdeveloped branch in this field, is given. The third chapter of the document describes aspects of using music software as a learning intent complemented with some examples of a freeware program. Additionally, a slight overview of digital (sheet)music archives together with some interesting examples is given in the fourth chapter. Also, the field-specific bibliography (comprising years 1989-2012) is presented in the fifth chapter. In consideration of the fact that almost all user interfaces of the software packages use English language, an illustrated English-Estonian dictionary of relevant terms is appended. The database contains 184 entries of topic-related software packages – 4 intelligent music stand applications, 13 digital sheet music converter applications, 98 score editors, 14 study assistant applications and 55 miscellaneous applications; 13 digital note archives and 113 publications

Optical Music Recognition

Nowadays records, radio, television and the internet spread music more widely than ever before, and an overwhelming number of musical works are available to us. During the last decades, a great interest in converting music scores into a computer-readable format has arisen, and with this the field of Optical Music Recognition. Optical Music Recognition (OMR) is the name of systems for music score recognition, and is similar to Optical Character Recognition (OCR) except that it is used to recognize musical symbols instead of letters. OMR systems try to automatically recognize the main musical objects of a scanned music score and convert them into a suitable electronic format, such as a MIDI file, an audio waveform or ABC Notation. The advantage of such a digital format, compared to retaining the whole image of a music score, is that only the semantics of music are stored, that is notes, pitches and durations, contextual information and other relevant information. This way much computer space is saved, and at the same time scores can be printed over and over again, without loss of quality, and they can be edited and played on a computer \citep{Vieira01}. OMR may also be used for educational reasons - to convert scores into Braille code for blind people, to generate customized version of music exercises etc. In addition, this technology can be used to index and collect scores in databases. Today, there are a number of on-line databases containing digital sheet music, making music easily available for everyone, free of charge. The earliest attempts at OMR were made in the early 1970's. During the last decades, OMR has been especially active, and there are currently a number of commercially available packages. The first commercial products came in the early 90's. However, in most cases these systems operate properly only with well-scanned documents of high quality. When it comes to precision and reliability, none of the commercial OMR systems solve the problem in a satisfactory way. The aim of this thesis is to study various existing OMR approaches and suggest novel methods, or modifications/improvements of current algorithms. The first stages of the process is prioritized, and we limit to concentrate on identifying the main musical symbols, essential for playing the melody, while text, slurs, staff numbering etc. are ignored by our program. The last part of an OMR program usually consists of correcting classification errors by introducing musical rules. In this thesis, this is only applied to correct wrongly classified pitched for accidentals