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

Using text mining techniques for classical music scores analysis

Music Classification is a particular area of Computational Musicology that provides valuable insights about the evolving of compo- sition patterns and assists in catalogue generation. The proposed work detaches from former works by classifying music based on music score in- formation. Text Mining techniques support music score processing while Classification techniques are used in the construction of decision mod- els. Although research is still at its earliest beginnings, the work already provides valuable contributes to symbolic music representation process- ing and subsequent analysis. Score processing involved the counting of ascending and descending chromatic intervals, note duration and meta- information tagging. Analysis involved feature selection and the evalu- ation of several data mining algorithms, ensuring extensibility towards larger repositories or more complex problems. Experiments report the analysis of composition epochs on a subset of the Mutopia project open archive of classical LilyPond-annotated music scores

A Digital Library of Folklore Songs and Keyword-Based Search Engine

ACM Computing Classification System (1998): H3.3, H.5.5, J5.We present a full text search engine in a digital library of Bulgarian folklore songs - in the lyrics and scores of the songs. The deployment of the digital library on the cloud as well as the technical requirements for providing our data to Europeana are discussed.This work is partially supported by Grant of the Bulgarian National Science Foundation under number DTK-02-54/2009

Application for Online Music Notation Typesetting

V tejto práci je predstavená aplikácia pre online zápis hudby s použitím SVG vektorovej grafiky v prostredí webových prehliadačov. Pozrieme sa na relevantné postupy pri notovom zápise a na históriu sadzby hudobného zápisu. Preskúmame a roztriedime existujúce programy na notový zápis. Na ich základe navrhneme užívateľské rozhranie pre aplikáciu s niekoľkými obmenami. Nasleduje popis implementácie aplikácie. Predstavíme tiež sadu funkcionalít, ktorých užitočnosť spolu s užívateľským rozhraním otestujeme na užívateľoch. Vyhodnotíme výsledky testovania a na jeho základe vylepšíme niektoré črty aplikácie. Nakoniec vyhodnotíme aplikáciu a jej budúci vývin. Keďže SVG sa stále považuje za experimentálnu technológiu a niektoré prehliadače ju stále nepodporujú alebo sa ich implementácia líši, bola aplikácia vytvorená tak, aby fungovala na prehliadači Chrome od spoločnosti Google.In this thesis, an application for musical notation in the environment of web browsers using the SVG vector graphics format is proposed. We have a look at relevant standard notation practices as well as at the history of musical typesetting. Existing programs for musical notation are classified and examined and based on those a user interface for the application is designed along with some modifications. The implementation of the application is then described. A set of features is presented and their usefulness is with the user interface tested on users. The feedback of the testing is then evaluated and the application is improved based on the feedback. Finally, the future of the application is discussed. Because SVG can still be considered an experimental technology and many browsers lack its support or their behaviour differs, the application is only guaranteed to work on the Google Chrome browser.

ESSYS* Sharing #UC: An Emotion-driven Audiovisual Installation

We present ESSYS* Sharing #UC, an audiovisual installation artwork that reflects upon the emotional context related to the university and the city of Coimbra, based on the data shared about them on Twitter. The installation was presented in an urban art gallery of C\'irculo de Artes Pl\'asticas de Coimbra during the summer and autumn of 2021. In the installation space, one may see a collection of typographic posters displaying the tweets and listening to an ever-changing ambient sound. The present audiovisuals are created by an autonomous computational creative approach, which employs a neural classifier to recognize the emotional context of a tweet and uses this resulting data as feedstock for the audiovisual generation. The installation's space is designed to promote an approach and blend between the online and physical perceptions of the same location. We applied multiple experiments with the proposed approach to evaluate the capability and performance. Also, we conduct interview-based evaluation sessions to understand how the installation elements, especially poster designs, are experienced by people regarding diversity, expressiveness and possible employment in other commercial and social scenarios.Comment: Paper to be published in 2022 IEEE VIS Arts Program (VISAP 2022). For the associated supplementary materials, see https://cdv.dei.uc.pt/essys_sharing_uc

Big data optical music recognition with multi images and multi recognisers

In this paper we describe work in progress towards Multi-OMR, an approach to Optical Music Recognition (OMR) which aims to significantly improve the accuracy of musical score digitisation. There are a large number of scores available in public databases, as well as a range of different commercial and open source OMR tools. Using these resources, we are exploring a Big Data approach to harnessing datasets by aligning and combining the results of multiple versions of the same score, processed with multiple technologies. It is anticipated that this approach will yield high quality results, opening up large datasets to researchers in the field of digital musicology

A system for the analysis of musical data

The role of music analysis is to enlighten our understanding of a piece of music. The role of musical performance analysis is to help us understand how a performer interprets a piece of music. The current work provides a tool which combines music analysis with performance analysis. By combining music and performance analysis in one system new questions can be asked of a piece of music: how is the structure of a piece reflected in the performance and how can the performance enlighten our understanding of the piece's structure? The current work describes a unified database which can store and present musical score alongside associated performance data and musical analysis. Using a general purpose representation language, Performance Mark-up Language (PML), aspects of performance are recorded and analysed. Data thus acquired from one project is made available to others. Presentation involves high-quality scores suitably annotated with the requested information. Such output is easily and directly accessible to musicians, performance scientists and analysts. We define a set of data structures and operators which can operate on musical pitch and musical time, and use them to form the basis of a query language for a musical database. The database can store musical information (score, gestural data, etc.). Querying the database results in annotations of the musical score. The database is capable of storing musical score information and performance data and cross-referencing them. It is equipped with the necessary primitives to execute music-analytical queries, and highlight notes identified from the score and display performance data alongside the score

Four Handel oratorio libretti published by John Gregory of Leicester, 1759 - 1774

George Frederic Handel had links with Leicestershire through the very wealthy writer and patron of music and literature Charles Jennens of Gopsall Hall, who, amongst other things wrote and prepared the texts for Messiah and several other important Handel works, and created the most comprehensive library of Handel scores and manuscripts. Between 1759 and 1774 the Leicester printer John Gregory, the founder and publisher of the Leicester Journal, printed and sold full texts of the libretti of four Handel oratorios: Messiah (ca. 1759), Judas Maccabaeus and Esther (both 1761) and Jephtha (1774). All were produced for specific Leicestershire performances of the works. The first three were part of the Church Langton ‘Music Meetings’ of William Hanbury, which included the first known church performance of Messiah in 1959. The fourth was printed for the 1774 Anniversary festival of the Leicester Infirmary. organised and funded by Joseph Cradock, one of Gregory’s fellow Governors of the Infirmary, and brought together many of the leading interpreters and scholars of Handel, creating what was widely regarded as the most important musical event ever held in Leicester

Automatic music transcription: challenges and future directions

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects