Finding What You Need, and Knowing What You Can Find: Digital Tools for Palaeographers in Musicology and Beyond

This chapter examines three projects that provide musicologists with a range of resources for managing and exploring their materials: DIAMM (Digital Image Archive of Medieval Music), CMME (Computerized Mensural Music Editing) and the software Gamera. Since 1998, DIAMM has been enhancing research of scholars worldwide by providing them with the best possible quality of digital images. In some cases these images are now the only access that scholars are permitted, since the original documents are lost or considered too fragile for further handling. For many sources, however, simply creating a very high-resolution image is not enough: sources are often damaged by age, misuse (usually Medieval ‘vandalism’), or poor conservation. To deal with damaged materials the project has developed methods of digital restoration using mainstream commercial software, which has revealed lost data in a wide variety of sources. The project also uses light sources ranging from ultraviolet to infrared in order to obtain better readings of erasures or material lost by heat or water damage. The ethics of digital restoration are discussed, as well as the concerns of the document holders. CMME and a database of musical sources and editions, provides scholars with a tool for making fluid editions and diplomatic transcriptions: without the need for a single fixed visual form on a printed page, a computerized edition system can utilize one editor’s transcription to create any number of visual forms and variant versions. Gamera, a toolkit for building document image recognition systems created by Ichiro Fujinaga is a broad recognition engine that grew out of music recognition, which can be adapted and developed to perform a number of tasks on both music and non-musical materials. Its application to several projects is discussed

Processing of Byzantine Neume Notation in Ancient Historical Manuscripts

This article presents the principal results of the doctoral thesis “Recognition of neume notation in historical documents” by Lasko Laskov (Institute of Mathematics and Informatics at Bulgarian Academy of Sciences), successfully defended before the Specialized Academic Council for Informatics and Mathematical Modelling on 07 June 2010.Byzantine neume notation is a specific form of note script, used by the Orthodox Christian Church since ancient times until nowadays for writing music and musical forms in sacred documents. Such documents are an object of extensive scientific research and naturally with the development of computer and information technologies the need of a software tool which can assist these efforts is needed. In this paper a set of algorithms for processing and analysis of Byzantine neume notation are presented which include document image segmentation, character feature vector extraction, classifier learning and character recognition. The described algorithms are implemented as an integrated scientific software system.* This work has been partly supported by Grant No. DTK 02/54, Bulgarian Science Fund, Ministry of Education, Youth and Science

Text and Transmission

The modern reader may encounter the Greek text of Euripides' surviving plays in many forms: in print either in complete editions or in separate editions of single plays published with translations or commentaries or both, and in digital form at well-known sites on the internet. When Euripides composed his plays, he is most likely to have written on a papyrus roll, although for rough drafts of small sections he could have used wax tablets, loose papyrus sheets, or pottery sherds. Although the papyrus rolls and early codices give us intriguing glimpses of the text of the Euripides plays up the seventh century CE, the surviving complete plays depend on the medieval textual tradition. For Euripides as for Aeschylus and Sophocles, Alexandrian scholars collected texts of as many plays as they could, comparing their titles to those known from the didascalic records. About seventy plays of Euripides never reached the medieval manuscript tradition

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

Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation

Staff detection and removal is one of the most important issues in optical music recognition (OMR) tasks since common approaches for symbol detection and classification are based on this process. Due to its complexity, staff detection and removal is often inaccurate, leading to a great number of errors in posterior stages. For this reason, a new approach that avoids this stage is proposed in this paper, which is expected to overcome these drawbacks. Our approach is put into practice in a case of study focused on scores written in white mensural notation. Symbol detection is performed by using the vertical projection of the staves. The cross-correlation operator for template matching is used at the classification stage. The goodness of our proposal is shown in an experiment in which our proposal attains an extraction rate of 96 % and a classification rate of 92 %, on average. The results found have reinforced the idea of pursuing a new research line in OMR systems without the need of the removal of staff lines.This work has been funded by the Ministerio de Educación, Cultura y Deporte of the Spanish Government under a FPU Fellowship No. AP20120939, by the Ministerio de Economía y Competitividad of the Spanish Government under Project No. TIN2013-48152-C2-1-R and Project No. TIN2013-47276-C6-2-R, by the Consejería de Educación de la Comunidad Valenciana under Project No. PROMETEO/2012/017 and by the Junta de Andalucía under Project No. P11-TIC-7154

Information Technology for Preserving the Bulgarian Folklore Heritage

Folk songs are an important and essential part of the Bulgarian cultural heritage. Following the traditions of the 20th century in publishing Bulgarian folk songs, we prepared the book “Folk Songs from Thrace” [3] with scores and lyrics recorded from original performances in the 60s and 80s of the last century. We created a digital library of over 1200 songs, which provides access to songs via full-text search engine. The data sources are stored using advanced information technology to encode texts, notes and sound. Traditional indexes and bookmarks for the book are also developed using our software

Application of Wavelet Decomposition to Document Line Segmentation

ACM Computing Classification System (1998): I.7, I.7.5.In this paper an approach to document line segmentation is presented. The algorithm is based on a wavelet transform of the horizontal projective profile of the document image. The projective profile is examined as a one-dimensional discrete signal which is decomposed using the pyramidal wavelet algorithm up to a precise scale, where local minima and maxima are discovered. These local extrema, projected into the input signal, correspond to the spacing between document lines and to the pivots of the lines. The method has been tested on a broad set of printed and handwritten documents and proven to be stable and efficient

Deep Neural Networks for Document Processing of Music Score Images

[EN] There is an increasing interest in the automatic digitization of medieval music documents. Despite efforts in this field, the detection of the different layers of information on these documents still poses difficulties. The use of Deep Neural Networks techniques has reported outstanding results in many areas related to computer vision. Consequently, in this paper, we study the so-called Convolutional Neural Networks (CNN) for performing the automatic document processing of music score images. This process is focused on layering the image into its constituent parts (namely, background, staff lines, music notes, and text) by training a classifier with examples of these parts. A comprehensive experimentation in terms of the configuration of the networks was carried out, which illustrates interesting results as regards to both the efficiency and effectiveness of these models. In addition, a cross-manuscript adaptation experiment was presented in which the networks are evaluated on a different manuscript from the one they were trained. The results suggest that the CNN is capable of adapting its knowledge, and so starting from a pre-trained CNN reduces (or eliminates) the need for new labeled data.This work was supported by the Social Sciences and Humanities Research Council of Canada, and Universidad de Alicante through grant GRE-16-04.Calvo-Zaragoza, J.; Castellanos, F.; Vigliensoni, G.; Fujinaga, I. (2018). Deep Neural Networks for Document Processing of Music Score Images. Applied Sciences. 8(5). https://doi.org/10.3390/app8050654S85Bainbridge, D., & Bell, T. (2001). Computers and the Humanities, 35(2), 95-121. doi:10.1023/a:1002485918032Byrd, D., & Simonsen, J. G. (2015). Towards a Standard Testbed for Optical Music Recognition: Definitions, Metrics, and Page Images. Journal of New Music Research, 44(3), 169-195. doi:10.1080/09298215.2015.1045424LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A. R. S., Guedes, C., & Cardoso, J. S. (2012). Optical music recognition: state-of-the-art and open issues. International Journal of Multimedia Information Retrieval, 1(3), 173-190. doi:10.1007/s13735-012-0004-6Louloudis, G., Gatos, B., Pratikakis, I., & Halatsis, C. (2008). Text line detection in handwritten documents. Pattern Recognition, 41(12), 3758-3772. doi:10.1016/j.patcog.2008.05.011Montagner, I. S., Hirata, N. S. T., & Hirata, R. (2017). Staff removal using image operator learning. Pattern Recognition, 63, 310-320. doi:10.1016/j.patcog.2016.10.002Calvo-Zaragoza, J., Micó, L., & Oncina, J. (2016). Music staff removal with supervised pixel classification. International Journal on Document Analysis and Recognition (IJDAR), 19(3), 211-219. doi:10.1007/s10032-016-0266-2Calvo-Zaragoza, J., Pertusa, A., & Oncina, J. (2017). Staff-line detection and removal using a convolutional neural network. Machine Vision and Applications, 28(5-6), 665-674. doi:10.1007/s00138-017-0844-4Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. doi:10.1109/tpami.2016.2572683Kato, Z. (2011). Markov Random Fields in Image Segmentation. Foundations and Trends® in Signal Processing, 5(1-2), 1-155. doi:10.1561/2000000035Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.72679

Becoming Byzantine: Modernization and Tradition in the Liturgical Music of the Greek Orthodox Church

When I walk into my Greek Orthodox Church on any given Sunday service, I am greeted by the sound of the past—florid melodies backed only by the single-note drone of another chanter. The exotic sounding notes echo in the cavernous cathedral as incense lightly stings my nose. Suddenly, from out of nowhere booms an organ, plunking out a major chord for the slightly out of tune choir that begins to sing in harmony. These two types of liturgical music seem to be at complete odds with one another, and yet, each holds a significant place in the Greek Orthodox tradition. However, their relationship is not always easy—the rapport between choirs and chanters is often tense, stemming from a conflict over orthopraxy. Church musicians are separated by what they believe to be the ‘correct’ music of the Church, pitting the Byzantine purists against choral enthusiasts. The Byzantine purists believe that as the original music of worship in the Greek Orthodox Church, Byzantine music is obviously more authentic to the experience of the Orthodox Christian. Choral enthusiasts, on the other hand, grew up hearing harmonized music in Greek Orthodox parishes of the United States, and thus reject the notion that Byzantine chant should have dominance