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His paper “Optimizing Feature Extraction for Symbolic Music” has been accepted for the ISMIR 2023 conference. The 24th conference of the International Society for Music Information Retrieval (ISMIR) will be held in Milan, Italy, from November 5-9, 2023. ISMIR is the largest research venue for scholars, researchers, practitioners, and industries in the field of Music Information Retrieval (MIR), an interdisciplinary research area for the processing, analysis, indexing, organizing and accessing music-related data.

The Study Day ‘Intersections: Interdisciplinary Approached to Opera’ will be taking place at the University of Cambridge on 27 June 2023. It can be followed both in person and virtually. For full details, including programme, abstracts, and bios, click here.

Conferences Parole del Metastasio: Opera and Emotions in Eighteenth-century Europe Universidad Complutense de Madrid, Biblioteca Histórica Marqués de Valdecilla | 4-7 May, 2022 Due to the COVID pandemic, the dates and location of the conference were changed. In eighteenth-century Europe, Metastasio’s work marked poetic, musical, and cultural boundaries within which the production of sung music developed for more than a hundred years. His vision of the world, and of human relationships and passions, influenced not only the hundreds of composers who ventured the musical setting of his lyrics, but also all other poets inspired by his words for music. Given the unparalleled impact of Metastasio’s texts in operatic composition throughout the eighteenth century, complete exploration of the librettist’s figure requires attending to the myriads of musical versions prompted by the emotions expressed in his lyrics. Therefore, we warmly invite presentations investigating the relationship between text and music around Metastasian passions, with especial attention to the dramaturgical and musical treatment thereof within his recitatives and closed pieces. We particularly encourage papers using novel methodologies for the study of the librettist’s world, including digital, computer-assisted techniques for data extraction from both music and poetry. This symposium is an expression of the European

DIDONE is an ERC-funded project jointly participated by the Instituto Complutense de Ciencias Musicales (ICCMU), the Universidad Complutense de Madrid (UCM), and the Universidad Carlos III de Madrid (UC3M). The project is led by Prof. Dr. Álvaro Torrente Sánchez-Guisande and involves 10 researchers, 4 music engravers, 3 correctors, and a project manager. Within DIDONE’s main aims are (1) to determine the compositional techniques whereby emotions were expressed in 18th century opera seria, (2) to develop a set of quantitative tools applicable to our and other repertoires, and (3) to create a digital corpus of ca. 3.000 arias. The postdoc researcher on ‘Musical data science’ is expected to engage and contribute in aims (1) and (2) in collaboration with DIDONE’s research team. S/he will work in tight collaboration with our music theorist, our senior statistician, and our software developer. S/he will be expected to focus on producing high-impact scientific literature at the interface between data/computer science and musicology, and on improving existent computational tools within the project that involve MusicXML and Python. We Offer Two-year contract, with expected start date in June 2022 or earlier. Gross annual salary within the range €25.000-€32.000, depending on experience. Possibility of salary increase upon the

Computer Science applications to MIR DIDONE Project Seminar Series 21 January 2021, 12am-1pm ECT Ana Llorens and Paula Muñoz-Lago   In this seminar, we will be presenting the computational methodology we are applying to the study of our musical scores in DIDONE.   First, we will discuss the general approach to data extraction and processing, with a special focus on the musical features we consider relevant to our corpus. Subsequently, and for the most part of the seminar, we will present our latest software for the analysis of melodic features in XML scores. In particular, the software retrieves and computes data on melodic configuration (non-enharmonic intervals, interval types, scale degrees, ambitus, leaps, etc.) in the form of Excel spreadsheets and provides accompanying visualisations for easier evaluation. It can process multiple files at a time and automatically groups the pieces for analysis according to select parameters (date, composer, key, etc.) and at various levels of filtering.   Finally, we will show some ongoing artificial intelligence applications on thusly-extracted data. These mainly focus on unsupervised learning for corpus clustering attending to compositional features. Although not necessarily restricted to it, such techniques will ultimately be aimed at determining whether or not there is